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<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves While each project involves unique tasks, contributors may: Design and evaluate Quarterly Business Review scenarios — performance vs.<br> benchmark, allocation drift against IPS tolerance bands, and material fund document changes; Create rebalancing and tax-loss harvesting test cases involving cost basis, short-term vs.<br> long-term capital gains, and wash-sale rule application; Develop suitability check cases probing risk tolerance, time horizon, prohibited holdings, and concentration limits against a client's Investment Policy Statement; Construct fee-stack analysis cases: portfolio-weighted expense ratios, advisory fees, and all-in cost comparisons against category benchmarks; Document test cases clearly with verified calculations, policy citations, and correct answers.<br> What we look for This opportunity is a good fit for professionals with a background in investment management, financial advisory, or broader financial services who are open to part-time, non-permanent projects.<br> Ideally, contributors will have: Degree in Finance, Economics, Business Administration, Accounting, Mathematics, Statistics, or any related field; 2+ years of finance, investment, accounting, or financial services experience; Current or recent experience in advisory &amp; management or adjacent roles; Practical knowledge of portfolio performance analysis, tax-lot accounting, wash-sale rules, and Investment Policy Statement literacy; a CFA, CFP, CPWA, CIMA, or CIPM credential is a strong positive signal; Strong written English (C1+).<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements.<br> This is an estimate, not a guaranteed workload, and applies only while the project is active.<br> Compensation On this project, contributors can earn up to $50 per hour equivalent , depending on their level and pace of contribution.<br> Compensation varies across projects depending on scope, complexity, and required expertise.<br> Please note that other projects on the platform may offer different earning levels based on their requirements.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves While each project involves unique tasks, contributors may: Evaluate AI-generated auto insurance claims decisions for accuracy, coverage correctness, and regulatory compliance; Design FNOL scenarios with deliberate contradictions, decoy files, and outdated documents to test agent robustness; Write and grade fraud-flagging scenarios using structured reason codes (late reporting, recently purchased policy, inconsistent damage) for SIU referral; Build subrogation test cases applying state-specific negligence rules (comparative vs.<br> contributory) and assess likelihood of recovery; Document test cases clearly with correct answers, policy citations, and payout calculations.<br> What we look for This opportunity is a good fit for professionals with a background in insurance claims, legal services, or broader financial services who are open to part-time, non-permanent projects.<br> Ideally, contributors will have: Degree in Insurance, Risk Management, Business Administration, Finance, Law, or any related field; 3+ years of insurance, claims, legal, or financial services experience; Current or recent experience in claims &amp; adjusting or adjacent roles; Familiarity with auto insurance coverage decisions, state-specific negligence rules, and adjuster authority-limit culture; AIC, CPCU, CIFI, or SCLA credential is a strong positive signal, though not required if hands-on experience is solid; Strong written English (C1+).<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements.<br> This is an estimate, not a guaranteed workload, and applies only while the project is active.<br> Compensation On this project, contributors can earn up to $50 per hour equivalent , depending on their level and pace of contribution.<br> Compensation varies across projects depending on scope, complexity, and required expertise.<br> Please note that other projects on the platform may offer different earning levels based on their requirements.<br></span> </div>

<p><h4>About Lucidya<\/h4>\n<p>Lucidya is an AI-native platform for customer experience (CX) intelligence that helps organizations understand, engage, and retain customers at scale.<\/p>\n<p>Unlike traditional platforms that stop at reporting and dashboards, Lucidya combines proprietary AI, deep language understanding, and autonomous workflows to help organizations move from insight to action. Our technology is built in-house and trained on millions of multilingual customer conversations, enabling brands across the MENA region to make better decisions, deliver better experiences, and build stronger customer relationships.<\/p>\n<p>As AI reshapes how businesses operate, we're building products that make customer understanding faster, more accurate, and more actionable than ever before.<\/p>\n\n<h4>Why this role matters<\/h4>\n<p>Most organizations today are overwhelmed with customer data.<\/p>\n<p>Conversations are happening across social media, surveys, reviews, support tickets, messaging channels, and countless other touchpoints. The challenge isn't collecting the data anymore. It's knowing what matters and what to do next.<\/p>\n<p>The Monitoring & Research domain sits at the center of that challenge.<\/p>\n<p>This is where customers come to understand public perception, identify emerging trends, monitor competitors, measure customer sentiment, and uncover the insights that drive business decisions. It is one of the most strategically important parts of Lucidya's platform and one of the biggest opportunities for innovation as AI transforms how research and customer intelligence are done.<\/p>\n<p>As Senior Product Manager, you'll be responsible for shaping how thousands of users discover, trust, and act on insights generated from millions of customer conversations.<\/p>\n<p>You'll be joining a team that values ownership over hierarchy, customer outcomes over feature output, and learning over certainty. We move quickly, challenge assumptions, and expect product leaders to bring strong opinions while remaining open to changing their minds when the evidence says otherwise.<\/p>\n<p>If you're excited by complex customer problems, AI-powered products, and the opportunity to define the future of customer intelligence in a region where world-class solutions are still emerging, this role will give you significant room to make an impact.<\/p>\n\n<h4>What you'll do<\/h4>\n<p><strong>Own the future of Monitoring & Research at Lucidya<\/strong><br>\nYou'll define the vision, strategy, and roadmap across three key product areas:<\/p>\n<ul>\n<li>Social listening<\/li>\n<li>Managed channels analytics<\/li>\n<li>Voice of Customer (VoC)<\/li>\n<\/ul>\n<p>Your responsibility isn't simply delivering features. It's ensuring customers can confidently answer critical business questions about their customers, markets, competitors, and brand performance.<\/p>\n\n<p><strong>Turn customer signals into decisions<\/strong><br>\nYou'll spend significant time understanding how customers currently make decisions, where they struggle to find trustworthy insights, and what prevents them from taking action.<\/p>\n<p>You'll use those learnings to build products that help customers move beyond reporting and toward clear recommendations, meaningful insights, and measurable business outcomes.<\/p>\n\n<p><strong>Lead discovery before delivery<\/strong><br>\nYou'll challenge assumptions, validate problems, and ensure we're solving issues that matter before resources are committed.<\/p>\n<p>You'll regularly engage customers, prospects, Customer Success teams, and internal stakeholders to uncover opportunities and validate product direction.<\/p>\n<p>Success in this role means building the right thing, not simply building things quickly.<\/p>\n\n<p><strong>Raise the quality bar for AI-powered insights<\/strong><br>\nAs AI becomes increasingly central to our platform, you'll help define what \"trustworthy AI\" means in practice.<\/p>\n<p>You'll work closely with AI, Data, Design, and Engineering teams to improve:<\/p>\n<ul>\n<li>Insight accuracy<\/li>\n<li>Sentiment quality<\/li>\n<li>Explainability<\/li>\n<li>Relevance<\/li>\n<li>Customer trust<\/li>\n<\/ul>\n<p>You'll constantly ask whether an insight is genuinely useful, not just technically impressive.<\/p>\n\n<p><strong>Make difficult prioritization decisions<\/strong><br>\nNot every good idea belongs on the roadmap.<\/p>\n<p>You'll evaluate opportunities using customer feedback, product data, market insights, commercial priorities, and strategic impact.<\/p>\n<p>You'll make trade-offs transparently and help the organization understand why certain bets are worth making while others are not.<\/p>\n\n<p><strong>Drive product adoption and business impact<\/strong><br>\nYou'll closely monitor customer behavior, adoption patterns, retention drivers, churn signals, and feedback loops.<\/p>\n<p>You'll use those insights to continuously improve the product and ensure the work being delivered creates measurable value for both customers and the business.<\/p>\n\n<p><strong>Be a leader beyond product<\/strong><br>\nYou'll collaborate across Engineering, Design, AI, Customer Success, Sales, Marketing, and Leadership teams.<\/p>\n<p>You'll help create alignment, provide clarity during ambiguity, and contribute to building a culture where teams are empowered to solve problems rather than simply execute requirements.<\/p>\n\n<h4>Requirements<\/h4>\n<h4>Who you are<\/h4>\n<ul>\n<li>You have strong product fundamentals<br>\nYou have at least 5 years of product management experience, ideally within B2B SaaS environments.<br>\nYou know how to take a problem from discovery through validation, delivery, measurement, and iteration.<br>\nYou understand that product management is ultimately about creating customer and business value, not managing backlogs.<\/li>\n<li>You simplify complexity<br>\nYou're comfortable working with large datasets, analytics platforms, AI-powered products, or technically complex systems.<br>\nMore importantly, you know how to transform complexity into experiences that feel intuitive and useful to customers.<\/li>\n<li>You stay close to customers<br>\nYou don't rely solely on internal opinions to make decisions.<br>\nYou regularly speak with customers, investigate workflows, validate assumptions, and seek evidence before committing to a direction.<br>\nYou're naturally curious about how people work and where products create friction in their lives.<\/li>\n<li>You are comfortable making decisions with incomplete information<br>\nYou won't always have perfect data.<br>\nYou can balance analysis with action, make informed decisions under uncertainty, and adapt when new information becomes available.<\/li>\n<li>You communicate with clarity<br>\nYou can explain technical concepts to non-technical audiences and customer problems to technical teams.<br>\nYou build alignment through clear thinking, structured communication, and transparency.<\/li>\n<li>You care about outcomes<br>\nYou measure success by customer adoption, customer value, and business impact rather than the number of features released.<br>\nYou're willing to challenge existing thinking when you believe there's a better way to solve a problem.<\/li>\n<\/ul>\n\n<h4>Bonus points if you have<\/h4>\n<ul>\n<li>Experience with social listening, media monitoring, Voice of Customer, customer intelligence, or research platforms<\/li>\n<li>Experience building AI-powered, analytics-heavy, or data-intensive products<\/li>\n<li>Familiarity with sentiment analysis, NLP, LLMs, recommendation systems, or automated research workflows<\/li>\n<li>Experience working with Arabic-language products, dialects, or regional customer behaviors<\/li>\n<li>Knowledge of MENA markets, particularly Saudi Arabia and the GCC<\/li>\n<li>Experience with platforms such as Brandwatch, Sprinklr, Meltwater, Talkwalker, Qualtrics, Medallia, or Brand24<\/li>\n<\/ul>\n\n<h4>What success looks like<\/h4>\n<ul>\n<li>Within your first 12 months:<\/li>\n<li>Customers trust Lucidya's insights enough to use them in critical business decisions<\/li>\n<li>Product adoption and engagement across Monitoring & Research increase meaningfully<\/li>\n<li>AI-powered insight experiences become more accurate, actionable, and differentiated<\/li>\n<li>Clear product strategy and prioritization frameworks are established across the domain<\/li>\n<li>Customer feedback consistently influences roadmap decisions<\/li>\n<li>Cross-functional teams have strong alignment around goals, priorities, and outcomes<\/li>\n<\/ul>\n\n<p>Lucidya is an equal opportunity employer. We believe great teams are built through diverse experiences, perspectives, and backgrounds. If you're excited about the opportunity but don't meet every requirement, we encourage you to apply anyway.<\/p><\/p><p><\/p>

<p><h4>About Lucidya<\/h4>\n<p>Lucidya is an AI-native platform for customer experience (CX) intelligence that helps organizations understand, engage, and retain customers at scale.<\/p>\n<p>Unlike traditional platforms that stop at reporting and dashboards, Lucidya combines proprietary AI, deep language understanding, and autonomous workflows to help organizations move from insight to action. Our technology is built in-house and trained on millions of multilingual customer conversations, enabling brands across the MENA region to make better decisions, deliver better experiences, and build stronger customer relationships.<\/p>\n<p>As AI reshapes how businesses operate, we're building products that make customer understanding faster, more accurate, and more actionable than ever before.<\/p>\n\n<h4>Why this role matters<\/h4>\n<p>Most organizations today are overwhelmed with customer data.<\/p>\n<p>Conversations are happening across social media, surveys, reviews, support tickets, messaging channels, and countless other touchpoints. The challenge isn't collecting the data anymore. It's knowing what matters and what to do next.<\/p>\n<p>The Monitoring & Research domain sits at the center of that challenge.<\/p>\n<p>This is where customers come to understand public perception, identify emerging trends, monitor competitors, measure customer sentiment, and uncover the insights that drive business decisions. It is one of the most strategically important parts of Lucidya's platform and one of the biggest opportunities for innovation as AI transforms how research and customer intelligence are done.<\/p>\n<p>As Senior Product Manager, you'll be responsible for shaping how thousands of users discover, trust, and act on insights generated from millions of customer conversations.<\/p>\n<p>You'll be joining a team that values ownership over hierarchy, customer outcomes over feature output, and learning over certainty. We move quickly, challenge assumptions, and expect product leaders to bring strong opinions while remaining open to changing their minds when the evidence says otherwise.<\/p>\n<p>If you're excited by complex customer problems, AI-powered products, and the opportunity to define the future of customer intelligence in a region where world-class solutions are still emerging, this role will give you significant room to make an impact.<\/p>\n\n<h4>What you'll do<\/h4>\n<p><strong>Own the future of Monitoring & Research at Lucidya<\/strong><br>\nYou'll define the vision, strategy, and roadmap across three key product areas:<\/p>\n<ul>\n<li>Social listening<\/li>\n<li>Managed channels analytics<\/li>\n<li>Voice of Customer (VoC)<\/li>\n<\/ul>\n<p>Your responsibility isn't simply delivering features. It's ensuring customers can confidently answer critical business questions about their customers, markets, competitors, and brand performance.<\/p>\n\n<p><strong>Turn customer signals into decisions<\/strong><br>\nYou'll spend significant time understanding how customers currently make decisions, where they struggle to find trustworthy insights, and what prevents them from taking action.<\/p>\n<p>You'll use those learnings to build products that help customers move beyond reporting and toward clear recommendations, meaningful insights, and measurable business outcomes.<\/p>\n\n<p><strong>Lead discovery before delivery<\/strong><br>\nYou'll challenge assumptions, validate problems, and ensure we're solving issues that matter before resources are committed.<\/p>\n<p>You'll regularly engage customers, prospects, Customer Success teams, and internal stakeholders to uncover opportunities and validate product direction.<\/p>\n<p>Success in this role means building the right thing, not simply building things quickly.<\/p>\n\n<p><strong>Raise the quality bar for AI-powered insights<\/strong><br>\nAs AI becomes increasingly central to our platform, you'll help define what \"trustworthy AI\" means in practice.<\/p>\n<p>You'll work closely with AI, Data, Design, and Engineering teams to improve:<\/p>\n<ul>\n<li>Insight accuracy<\/li>\n<li>Sentiment quality<\/li>\n<li>Explainability<\/li>\n<li>Relevance<\/li>\n<li>Customer trust<\/li>\n<\/ul>\n<p>You'll constantly ask whether an insight is genuinely useful, not just technically impressive.<\/p>\n\n<p><strong>Make difficult prioritization decisions<\/strong><br>\nNot every good idea belongs on the roadmap.<\/p>\n<p>You'll evaluate opportunities using customer feedback, product data, market insights, commercial priorities, and strategic impact.<\/p>\n<p>You'll make trade-offs transparently and help the organization understand why certain bets are worth making while others are not.<\/p>\n\n<p><strong>Drive product adoption and business impact<\/strong><br>\nYou'll closely monitor customer behavior, adoption patterns, retention drivers, churn signals, and feedback loops.<\/p>\n<p>You'll use those insights to continuously improve the product and ensure the work being delivered creates measurable value for both customers and the business.<\/p>\n\n<p><strong>Be a leader beyond product<\/strong><br>\nYou'll collaborate across Engineering, Design, AI, Customer Success, Sales, Marketing, and Leadership teams.<\/p>\n<p>You'll help create alignment, provide clarity during ambiguity, and contribute to building a culture where teams are empowered to solve problems rather than simply execute requirements.<\/p>\n\n<h4>Requirements<\/h4>\n<h4>Who you are<\/h4>\n<ul>\n<li>You have strong product fundamentals<br>\nYou have at least 5 years of product management experience, ideally within B2B SaaS environments.<br>\nYou know how to take a problem from discovery through validation, delivery, measurement, and iteration.<br>\nYou understand that product management is ultimately about creating customer and business value, not managing backlogs.<\/li>\n<li>You simplify complexity<br>\nYou're comfortable working with large datasets, analytics platforms, AI-powered products, or technically complex systems.<br>\nMore importantly, you know how to transform complexity into experiences that feel intuitive and useful to customers.<\/li>\n<li>You stay close to customers<br>\nYou don't rely solely on internal opinions to make decisions.<br>\nYou regularly speak with customers, investigate workflows, validate assumptions, and seek evidence before committing to a direction.<br>\nYou're naturally curious about how people work and where products create friction in their lives.<\/li>\n<li>You are comfortable making decisions with incomplete information<br>\nYou won't always have perfect data.<br>\nYou can balance analysis with action, make informed decisions under uncertainty, and adapt when new information becomes available.<\/li>\n<li>You communicate with clarity<br>\nYou can explain technical concepts to non-technical audiences and customer problems to technical teams.<br>\nYou build alignment through clear thinking, structured communication, and transparency.<\/li>\n<li>You care about outcomes<br>\nYou measure success by customer adoption, customer value, and business impact rather than the number of features released.<br>\nYou're willing to challenge existing thinking when you believe there's a better way to solve a problem.<\/li>\n<\/ul>\n\n<h4>Bonus points if you have<\/h4>\n<ul>\n<li>Experience with social listening, media monitoring, Voice of Customer, customer intelligence, or research platforms<\/li>\n<li>Experience building AI-powered, analytics-heavy, or data-intensive products<\/li>\n<li>Familiarity with sentiment analysis, NLP, LLMs, recommendation systems, or automated research workflows<\/li>\n<li>Experience working with Arabic-language products, dialects, or regional customer behaviors<\/li>\n<li>Knowledge of MENA markets, particularly Saudi Arabia and the GCC<\/li>\n<li>Experience with platforms such as Brandwatch, Sprinklr, Meltwater, Talkwalker, Qualtrics, Medallia, or Brand24<\/li>\n<\/ul>\n\n<h4>What success looks like<\/h4>\n<ul>\n<li>Within your first 12 months:<\/li>\n<li>Customers trust Lucidya's insights enough to use them in critical business decisions<\/li>\n<li>Product adoption and engagement across Monitoring & Research increase meaningfully<\/li>\n<li>AI-powered insight experiences become more accurate, actionable, and differentiated<\/li>\n<li>Clear product strategy and prioritization frameworks are established across the domain<\/li>\n<li>Customer feedback consistently influences roadmap decisions<\/li>\n<li>Cross-functional teams have strong alignment around goals, priorities, and outcomes<\/li>\n<\/ul>\n\n<p>Lucidya is an equal opportunity employer. We believe great teams are built through diverse experiences, perspectives, and backgrounds. If you're excited about the opportunity but don't meet every requirement, we encourage you to apply anyway.<\/p><\/p><p><\/p>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>A leading global pharmaceutical company is seeking an AI Solutions Architect who is responsible for designing, governing, delivering, and evolving the enterprise AI technical architecture across the company. Acting as the technical authority and delivery lead for AI within the AI Centre of Excellence, this role defines architectural standards, evaluates and governs AI platforms and solutions, prioritising off-the-shelf and vendor-led approaches, and ensures that all AI initiatives are scalable, secure, compliant, and aligned with the company's digital transformation strategy. The AI Solutions Architect works closely with AI Engineers, IT Business Partners, AI Champions, and business function leaders to assess technical feasibility, lead end-to-end solution delivery, support AI Proof of Value (POV) execution, and translate strategic AI ambitions into sound, future-proof solutions, ensuring every initiative is delivered on a technically rigorous and governance-compliant foundation.</p><p>Responsibilities:</p><ol><li>Strategy &amp; Architecture Leadership<ul><li>Define and own the enterprise AI technical architecture framework, including reference architectures, design patterns, integration blueprints, and technology standards across cloud and on-premises environments</li><li>Collaborate with the Architecture advisory board to ensure the AI architecture roadmap is aligned with the company's overall digital transformation strategy and enterprise IT architecture principles</li><li>Evaluate emerging AI technologies, frameworks, and platforms, providing technical direction and recommendations to senior IT and business stakeholders</li><li>Lead architectural governance for all AI initiatives, ensuring solutions adhere to approved standards, security requirements, regulatory constraints, and scalability principles</li><li>Define and maintain the enterprise AI technology stack, including cloud AI services (Azure, AWS, GCP), MLOps platforms, data platforms, and integration middleware</li></ul></li><li>AI Solution Design &amp; Technical Oversight<ul><li>Architect end-to-end AI solutions across the company's key business domains including Manufacturing, Quality &amp; Regulatory Affairs, R&amp;D, Commercial, Supply Chain, HR, and Finance</li><li>Produce high-quality architecture deliverables including solution design documents, architecture decision records (ADRs), technical specifications, data flow diagrams, and integration architecture blueprints</li><li>Lead technical design reviews and architecture assessments for all AI initiatives in the portfolio, ensuring fitness for purpose, scalability, and compliance</li><li>Define AI integration patterns with enterprise systems including SAP, MES, LIMS, CRM, and M365 ecosystems, ensuring seamless interoperability</li><li>Guide AI Developers in translating architecture designs into well-structured, maintainable, and production-ready solutions</li><li>Oversee the design of MLOps pipelines including model training, validation, deployment, monitoring, and retraining workflows</li></ul></li><li>Platform Engineering &amp; Infrastructure<ul><li>Own the architecture and governance of enterprise AI platforms including Microsoft Azure AI, Azure Machine Learning, Microsoft 365 Copilot, and other approved AI tooling</li><li>Define cloud infrastructure architecture for AI workloads including compute, storage, networking, and security configurations aligned with the company IT standards</li><li>Establish standards for model lifecycle management including versioning, registry, performance monitoring, drift detection, and retraining triggers</li><li>Drive the design of data architecture components critical to AI including feature stores, data lakes, vector databases, and real-time data pipelines in collaboration with the Data &amp; Analytics team</li><li>Ensure AI platforms meet GxP validation, 21 CFR Part 11, and audit trail requirements where applicable</li></ul></li><li>Governance, Compliance &amp; Responsible AI<ul><li>Embed regulatory and compliance requirements, including FDA AI/ML guidance, EMA requirements, GxP, GDPR, and HIPAA, into AI architecture design and review processes</li><li>Define and enforce responsible AI architectural guardrails including model explainability, bias detection, fairness assessments, and human-in-the-loop design patterns</li><li>Maintain AI architecture governance documentation including standards, patterns, approved toolsets, and deviation processes within the enterprise AI knowledge repository</li><li>Coordinate with IT Security, Data Privacy, Legal, Quality Assurance, and Regulatory Affairs to ensure AI solutions meet all applicable oversight requirements</li><li>Support E-AIAB governance processes by providing technical input into initiative assessments, vendor evaluations, and POV planning</li></ul></li><li>Technical Leadership &amp; Enablement<ul><li>Provide technical mentorship, code and architecture reviews, and hands-on guidance to the AI Developer team</li><li>Define engineering best practices, coding standards, and DevOps/MLOps conventions for the AI team</li><li>Collaborate with external vendors, implementation partners, and cloud providers to assess solutions, conduct technical due diligence, and ensure delivery quality</li><li>Contribute technical expertise to vendor RFP/RFI processes, proof-of-concept evaluations, and contract assessments</li><li>Represent the company's AI technical standards in cross-functional project delivery teams and steering committees</li></ul></li><li>Stakeholder Engagement &amp; Communication<ul><li>Translate complex technical architecture concepts into clear, accessible language for business stakeholders, executive leadership, and non-technical audiences</li><li>Serve as the primary technical escalation point for AI platform issues, architecture deviations, and integration challenges</li><li>Collaborate with IT Business Partners and AI Champions to provide technical feasibility input into AI opportunity assessments</li><li>Participate in external pharmaceutical AI forums, cloud provider events, and technology conferences to maintain leading-edge awareness and contribute to the company's technical reputation</li></ul></li></ol></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><ul><li>Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or related technical field</li><li>Master's degree in Artificial Intelligence, Data Science, Computer Science, or related discipline is preferred</li><li>Microsoft Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent cloud architecture certification is preferred</li><li>TOGAF or equivalent enterprise architecture certification is preferred</li><li>7-10 years of professional experience in IT, software engineering, data &amp; analytics, or AI/ML implementation</li><li>4-6 years of hands-on experience designing and delivering AI/ML solutions on cloud platforms (Azure, AWS, or GCP) in a production environment</li><li>Proven track record of owning end-to-end AI solution architecture in a complex, cross-functional enterprise environment</li><li>Experience with Microsoft Azure AI, Azure Machine Learning, and Microsoft 365 Copilot architecture and deployment is preferred</li><li>Pharmaceutical, healthcare, life sciences, or other regulated industry experience is preferred</li><li>Experience with GxP validation, 21 CFR Part 11, or regulatory technology compliance in an AI/ML context is preferred</li></ul><p>Skills:</p><p><strong>Technical Competencies:</strong></p><ul><li>AI/ML Architecture &amp; Solution Design</li><li>Cloud Platform Architecture (Azure / AWS / GCP)</li><li>ML Ops &amp; Model Lifecycle Management</li><li>Enterprise Integration &amp; API Design</li><li>Data Architecture &amp; Data Engineering</li><li>AI Governance, Ethics &amp; Responsible AI</li><li>Pharmaceutical Regulatory Compliance (GxP, FDA, EMA)</li></ul><p><strong>AI &amp; Technology Skills:</strong></p><ul><li>Deep expertise in AI/ML architecture patterns, including supervised/unsupervised learning, NLP, computer vision, generative AI, and LLM-based solution design</li><li>Strong hands-on proficiency with Azure AI Services, Azure Machine Learning, MLflow, or equivalent MLOps tooling</li><li>Solid experience designing and deploying generative AI solutions including RAG architectures, LLM orchestration (LangChain, Semantic Kernel), and enterprise copilot patterns</li><li>Strong command of enterprise integration architecture including REST APIs, event-driven architecture, message queues, and middleware platforms</li><li>Proficiency in cloud infrastructure design including IaC (Terraform, Bicep), containerization (Docker, Kubernetes), and CI/CD pipelines</li><li>Strong understanding of data architecture components including data lakes, lakehouses, feature stores, and vector databases</li><li>Working knowledge of pharmaceutical business processes including GxP operations, quality management systems, and regulatory affairs workflows (Preferred)</li><li>Solid understanding of AI governance frameworks, responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security principles relevant to AI deployment</li></ul><p></p></section>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>A leading global pharmaceutical company is seeking an AI Solutions Architect who is responsible for designing, governing, delivering, and evolving the enterprise AI technical architecture across the company.<br> Acting as the technical authority and delivery lead for AI within the AI Centre of Excellence, this role defines architectural standards, evaluates and governs AI platforms and solutions, prioritising off-the-shelf and vendor-led approaches, and ensures that all AI initiatives are scalable, secure, compliant, and aligned with the company's digital transformation strategy.<br> The AI Solutions Architect works closely with AI Engineers, IT Business Partners, AI Champions, and business function leaders to assess technical feasibility, lead end-to-end solution delivery, support AI Proof of Value (POV) execution, and translate strategic AI ambitions into sound, future-proof solutions, ensuring every initiative is delivered on a technically rigorous and governance-compliant foundation.<br> Responsibilities: 1.<br> Strategy &amp; Architecture Leadership Define and own the enterprise AI technical architecture framework, including reference architectures, design patterns, integration blueprints, and technology standards across cloud and on-premises environments Collaborate with the Architecture advisory board to ensure the AI architecture roadmap is aligned with the company's overall digital transformation strategy and enterprise IT architecture principles Evaluate emerging AI technologies, frameworks, and platforms, providing technical direction and recommendations to senior IT and business stakeholders Lead architectural governance for all AI initiatives, ensuring solutions adhere to approved standards, security requirements, regulatory constraints, and scalability principles Define and maintain the enterprise AI technology stack, including cloud AI services (Azure, AWS, GCP), MLOps platforms, data platforms, and integration middleware 2.<br> AI Solution Design &amp; Technical Oversight Architect end-to-end AI solutions across the company's key business domains including Manufacturing, Quality &amp; Regulatory Affairs, R&amp;D, Commercial, Supply Chain, HR, and Finance Produce high-quality architecture deliverables including solution design documents, architecture decision records (ADRs), technical specifications, data flow diagrams, and integration architecture blueprints Lead technical design reviews and architecture assessments for all AI initiatives in the portfolio, ensuring fitness for purpose, scalability, and compliance Define AI integration patterns with enterprise systems including SAP, MES, LIMS, CRM, and M365 ecosystems, ensuring seamless interoperability Guide AI Developers in translating architecture designs into well-structured, maintainable, and production-ready solutions Oversee the design of MLOps pipelines including model training, validation, deployment, monitoring, and retraining workflows 3.<br> Platform Engineering &amp; Infrastructure Own the architecture and governance of enterprise AI platforms including Microsoft Azure AI, Azure Machine Learning, Microsoft 365 Copilot, and other approved AI tooling Define cloud infrastructure architecture for AI workloads including compute, storage, networking, and security configurations aligned with the company IT standards Establish standards for model lifecycle management including versioning, registry, performance monitoring, drift detection, and retraining triggers Drive the design of data architecture components critical to AI including feature stores, data lakes, vector databases, and real-time data pipelines in collaboration with the Data &amp; Analytics team Ensure AI platforms meet GxP validation, 21 CFR Part 11, and audit trail requirements where applicable 4.<br> Governance, Compliance &amp; Responsible AI Embed regulatory and compliance requirements, including FDA AI/ML guidance, EMA requirements, GxP, GDPR, and HIPAA, into AI architecture design and review processes Define and enforce responsible AI architectural guardrails including model explainability, bias detection, fairness assessments, and human-in-the-loop design patterns Maintain AI architecture governance documentation including standards, patterns, approved toolsets, and deviation processes within the enterprise AI knowledge repository Coordinate with IT Security, Data Privacy, Legal, Quality Assurance, and Regulatory Affairs to ensure AI solutions meet all applicable oversight requirements Support E-AIAB governance processes by providing technical input into initiative assessments, vendor evaluations, and POV planning 5.<br> Technical Leadership &amp; Enablement Provide technical mentorship, code and architecture reviews, and hands-on guidance to the AI Developer team Define engineering best practices, coding standards, and DevOps/MLOps conventions for the AI team Collaborate with external vendors, implementation partners, and cloud providers to assess solutions, conduct technical due diligence, and ensure delivery quality Contribute technical expertise to vendor RFP/RFI processes, proof-of-concept evaluations, and contract assessments Represent the company's AI technical standards in cross-functional project delivery teams and steering committees 6.<br> Stakeholder Engagement &amp; Communication Translate complex technical architecture concepts into clear, accessible language for business stakeholders, executive leadership, and non-technical audiences Serve as the primary technical escalation point for AI platform issues, architecture deviations, and integration challenges Collaborate with IT Business Partners and AI Champions to provide technical feasibility input into AI opportunity assessments Participate in external pharmaceutical AI forums, cloud provider events, and technology conferences to maintain leading-edge awareness and contribute to the company's technical reputation Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or related technical field Master's degree in Artificial Intelligence, Data Science, Computer Science, or related discipline is preferred Microsoft Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent cloud architecture certification is preferred TOGAF or equivalent enterprise architecture certification is preferred 7–10 years of professional experience in IT, software engineering, data &amp; analytics, or AI/ML implementation 4–6 years of hands-on experience designing and delivering AI/ML solutions on cloud platforms (Azure, AWS, or GCP) in a production environment Proven track record of owning end-to-end AI solution architecture in a complex, cross-functional enterprise environment Experience with Microsoft Azure AI, Azure Machine Learning, and Microsoft 365 Copilot architecture and deployment is preferred Pharmaceutical, healthcare, life sciences, or other regulated industry experience is preferred Experience with GxP validation, 21 CFR Part 11, or regulatory technology compliance in an AI/ML context is preferred Skills: Technical Competencies: AI/ML Architecture &amp; Solution Design - Cloud Platform Architecture (Azure / AWS / GCP) - ML Ops &amp; Model Lifecycle Management - Enterprise Integration &amp; API Design - Data Architecture &amp; Data Engineering - AI Governance, Ethics &amp; Responsible AI - Pharmaceutical Regulatory Compliance (GxP, FDA, EMA) AI &amp; Technology Skills: Deep expertise in AI/ML architecture patterns, including supervised/unsupervised learning, NLP, computer vision, generative AI, and LLM-based solution design Strong hands-on proficiency with Azure AI Services, Azure Machine Learning, MLflow, or equivalent MLOps tooling Solid experience designing and deploying generative AI solutions including RAG architectures, LLM orchestration (LangChain, Semantic Kernel), and enterprise copilot patterns Strong command of enterprise integration architecture including REST APIs, event-driven architecture, message queues, and middleware platforms Proficiency in cloud infrastructure design including IaC (Terraform, Bicep), containerization (Docker, Kubernetes), and CI/CD pipelines Strong understanding of data architecture components including data lakes, lakehouses, feature stores, and vector databases Working knowledge of pharmaceutical business processes including GxP operations, quality management systems, and regulatory affairs workflows (Preferred) Solid understanding of AI governance frameworks, responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security principles relevant to AI deployment</span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br>What this opportunity involves: We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT: Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for: 8+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard: Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidEffort estimate Tasks for this project are estimated to take 30 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation: Up to $150/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~30 hours each; you set your own schedule.<br></span> </div>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at ~20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at ~20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description</h4> <p>Please submit your CV in English and indicate your level of English proficiency.</p> <p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.</p> <h4>What this opportunity involves</h4> <p>While each project involves unique tasks, contributors may:</p> <ul> <li>Design original computational physics problems that simulate real physics research workflows;</li> <li>Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy);</li> <li>Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks);</li> <li>Develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics;</li> <li>Base problems on real research challenges or practical applications from physics practice;</li> <li>Verify solutions using Python with standard physics simulation libraries;</li> <li>Document problem statements clearly and provide verified correct answers.</li> </ul> <h4>What we look for</h4> <p>This opportunity is a good fit for physicists with experience in Python open to part-time, non-permanent projects. Ideally, contributors will have:</p> <ul> <li>Degree in Physics (Theoretical, Experimental, or Computational) or related fields;</li> <li>Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent;</li> <li>2+ years of professional experience: applied, research, or teaching experience is applicable;</li> <li>Experience with numerical simulation methods;</li> <li>Ability to design problems that mirror real physics research workflows;</li> <li>Creative thinking in problem design across diverse physics areas;</li> <li>Familiarity with physics modeling and approximation techniques;</li> <li>Strong written English (C1+).</li> </ul> <h4>How it works</h4> <p>Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid</p> <h4>Project time expectations</h4> <p>For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.</p> <h4>Compensation</h4> <p>On this project, contributors can earn up to $35 per hour equivalent, depending on their level and pace of contribution.</p> <p>Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.</p></p><p></p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n <li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n <li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n <li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n <li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n <li>Not data labeling<\/li>\n <li>Not prompt engineering<\/li>\n <li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n <li>5+ years in software development<\/li>\n <li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n <li>Experience writing tests (functional, integration)<\/li>\n <li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n <li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n <li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n <li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n <li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n <li>Not data labeling<\/li>\n <li>Not prompt engineering<\/li>\n <li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n <li>5+ years in software development<\/li>\n <li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n <li>Experience writing tests (functional, integration)<\/li>\n <li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at ~20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at ~20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description</h4> <p>Please submit your CV in English and indicate your level of English proficiency.</p> <p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.</p> <h4>What this opportunity involves</h4> <p>While each project involves unique tasks, contributors may:</p> <ul> <li>Design and evaluate rule application scenarios — reading a compliance artifact (advertisement, disclosure, policy) against a specific regulation and citing the exact subsection that applies or is violated;</li> <li>Write sanctions alert disposition cases with structured false-positive reasoning documentation and escalation scenarios;</li> <li>Build compliance testing scenarios: sample selection, test design, findings documentation, and examiner-ready conclusion memos;</li> <li>Develop policy gap identification cases where an internal policy is silent on a regulatory requirement, contradicts the regulation, or has not been updated to reflect a recent change;</li> <li>Document all test cases with verified regulatory citations to the specific subsection — never approximated, never fabricated.</li> </ul> <h4>What we look for</h4> <p>This opportunity is a good fit for professionals with a background in compliance, regulatory affairs, legal services, or broader financial services who are open to part-time, non-permanent projects. Ideally, contributors will have:</p> <ul> <li>Degree in Law, Finance, Economics, Business Administration, Accounting, or any related field;</li> <li>2+ years of compliance, audit, legal, risk management, or financial services experience;</li> <li>Current or recent experience in compliance and regulatory roles, financial crimes, or adjacent roles;</li> <li>Citation discipline as a core habit — able to identify the exact regulatory subsection that applies and flag uncertainty rather than approximate;</li> <li>CRCM, CAMS, or equivalent local credential is a strong positive signal; former regulatory examiner or inspector status is equally strong;</li> <li>Strong written English (C1+).</li> </ul> <h4>How it works</h4> <p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.</p> <h4>Project time expectations</h4> <p>For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.</p> <h4>Compensation</h4> <p>On this project, contributors can earn up to $50 per hour equivalent, depending on their level and pace of contribution.</p> <p>Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.</p></p><p></p>

<p><h4>Description</h4> <p>Please submit your CV in English and indicate your level of English proficiency.</p> <p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.</p> <h4>What this opportunity involves</h4> <p>While each project involves unique tasks, contributors may:</p> <ul> <li>Design and evaluate rule application scenarios — reading a compliance artifact (advertisement, disclosure, policy) against a specific regulation and citing the exact subsection that applies or is violated;</li> <li>Write sanctions alert disposition cases with structured false-positive reasoning documentation and escalation scenarios;</li> <li>Build compliance testing scenarios: sample selection, test design, findings documentation, and examiner-ready conclusion memos;</li> <li>Develop policy gap identification cases where an internal policy is silent on a regulatory requirement, contradicts the regulation, or has not been updated to reflect a recent change;</li> <li>Document all test cases with verified regulatory citations to the specific subsection — never approximated, never fabricated.</li> </ul> <h4>What we look for</h4> <p>This opportunity is a good fit for professionals with a background in compliance, regulatory affairs, legal services, or broader financial services who are open to part-time, non-permanent projects. Ideally, contributors will have:</p> <ul> <li>Degree in Law, Finance, Economics, Business Administration, Accounting, or any related field;</li> <li>2+ years of compliance, audit, legal, risk management, or financial services experience;</li> <li>Current or recent experience in compliance and regulatory roles, financial crimes, or adjacent roles;</li> <li>Citation discipline as a core habit — able to identify the exact regulatory subsection that applies and flag uncertainty rather than approximate;</li> <li>CRCM, CAMS, or equivalent local credential is a strong positive signal; former regulatory examiner or inspector status is equally strong;</li> <li>Strong written English (C1+).</li> </ul> <h4>How it works</h4> <p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.</p> <h4>Project time expectations</h4> <p>For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.</p> <h4>Compensation</h4> <p>On this project, contributors can earn up to $50 per hour equivalent, depending on their level and pace of contribution.</p> <p>Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.</p></p><p></p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description</h4> <p>Please submit your CV in English and indicate your level of English proficiency.</p> <p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.</p> <h4>What this opportunity involves</h4> <p>While each project involves unique tasks, contributors may:</p> <ul> <li>Design and evaluate rule application scenarios — reading a compliance artifact (advertisement, disclosure, policy) against a specific regulation and citing the exact subsection that applies or is violated;</li> <li>Write sanctions alert disposition cases with structured false-positive reasoning documentation and escalation scenarios;</li> <li>Build compliance testing scenarios: sample selection, test design, findings documentation, and examiner-ready conclusion memos;</li> <li>Develop policy gap identification cases where an internal policy is silent on a regulatory requirement, contradicts the regulation, or has not been updated to reflect a recent change;</li> <li>Document all test cases with verified regulatory citations to the specific subsection — never approximated, never fabricated.</li> </ul> <h4>What we look for</h4> <p>This opportunity is a good fit for professionals with a background in compliance, regulatory affairs, legal services, or broader financial services who are open to part-time, non-permanent projects. Ideally, contributors will have:</p> <ul> <li>Degree in Law, Finance, Economics, Business Administration, Accounting, or any related field;</li> <li>2+ years of compliance, audit, legal, risk management, or financial services experience;</li> <li>Current or recent experience in compliance and regulatory roles, financial crimes, or adjacent roles;</li> <li>Citation discipline as a core habit — able to identify the exact regulatory subsection that applies and flag uncertainty rather than approximate;</li> <li>CRCM, CAMS, or equivalent local credential is a strong positive signal; former regulatory examiner or inspector status is equally strong;</li> <li>Strong written English (C1+).</li> </ul> <h4>How it works</h4> <p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.</p> <h4>Project time expectations</h4> <p>For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.</p> <h4>Compensation</h4> <p>On this project, contributors can earn up to $50 per hour equivalent, depending on their level and pace of contribution.</p> <p>Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.</p></p><p></p>

<p><h4>Description</h4> <p>Please submit your CV in English and indicate your level of English proficiency.</p> <p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.</p> <h4>What this opportunity involves</h4> <p>While each project involves unique tasks, contributors may:</p> <ul> <li>Design and evaluate rule application scenarios — reading a compliance artifact (advertisement, disclosure, policy) against a specific regulation and citing the exact subsection that applies or is violated;</li> <li>Write sanctions alert disposition cases with structured false-positive reasoning documentation and escalation scenarios;</li> <li>Build compliance testing scenarios: sample selection, test design, findings documentation, and examiner-ready conclusion memos;</li> <li>Develop policy gap identification cases where an internal policy is silent on a regulatory requirement, contradicts the regulation, or has not been updated to reflect a recent change;</li> <li>Document all test cases with verified regulatory citations to the specific subsection — never approximated, never fabricated.</li> </ul> <h4>What we look for</h4> <p>This opportunity is a good fit for professionals with a background in compliance, regulatory affairs, legal services, or broader financial services who are open to part-time, non-permanent projects. Ideally, contributors will have:</p> <ul> <li>Degree in Law, Finance, Economics, Business Administration, Accounting, or any related field;</li> <li>2+ years of compliance, audit, legal, risk management, or financial services experience;</li> <li>Current or recent experience in compliance and regulatory roles, financial crimes, or adjacent roles;</li> <li>Citation discipline as a core habit — able to identify the exact regulatory subsection that applies and flag uncertainty rather than approximate;</li> <li>CRCM, CAMS, or equivalent local credential is a strong positive signal; former regulatory examiner or inspector status is equally strong;</li> <li>Strong written English (C1+).</li> </ul> <h4>How it works</h4> <p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.</p> <h4>Project time expectations</h4> <p>For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.</p> <h4>Compensation</h4> <p>On this project, contributors can earn up to $50 per hour equivalent, depending on their level and pace of contribution.</p> <p>Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.</p></p><p></p>