Python Developer Jobs in Jordan

<p><h4>Job purpose</h4> <p>As a senior DevOps engineer, you will own the design, reliability, and scalability of our cloud-based production systems. You will lead the architecture, automation, and operation of our Azure/AWS infrastructure, drive security, performance, and cost efficiency across our platforms, and set DevOps standards and best practices for the team. You will act as a technical authority on cloud operations, mentor junior engineers, and partner with engineering leadership to shape the platform roadmap.</p> <h4>Duties & responsibilities</h4> <h4>Cloud system operations</h4> <ul> <li>Lead the design, deployment, and operation of Azure/AWS cloud-based production systems.</li> <li>Own system performance, incident response, and root-cause analysis across production applications.</li> <li>Define release engineering and pre-production validation standards to ensure system quality and functionality.</li> <li>Architect and enforce backup, disaster recovery, and cost optimization (FinOps) strategies across cloud environments.</li> <li>Lead container orchestration and workload management on AKS/Kubernetes clusters, including upgrades, scaling, and hardening.</li> </ul> <h4>Automation and scripting</h4> <ul> <li>Design and maintain enterprise-grade automation frameworks for operational and platform processes.</li> <li>Build reusable tooling and scripts (e.g., Python, Bash, PowerShell) for automation, observability, and incident response.</li> <li>Lead GitOps adoption and continuous-delivery practices using ArgoCD or Flux.</li> </ul> <h4>Security and compliance</h4> <ul> <li>Define and enforce cloud security best practices, IAM policies, and secrets management across environments.</li> <li>Establish and maintain security protocols and compliance posture (e.g., ISO 27001, SOC 2 controls relevant to infrastructure).</li> </ul> <h4>Monitoring and metrics</h4> <ul> <li>Architect and operate observability platforms (metrics, logging, tracing) across Azure/AWS, defining SLOs, SLIs, and alerting strategy.</li> <li>Drive operational excellence by analyzing reliability metrics and leading post-incident reviews and improvement initiatives.</li> </ul> <h4>Research and evaluation</h4> <ul> <li>Evaluate and recommend emerging technologies, tools, and architectural patterns for adoption.</li> <li>Lead vendor and product evaluations, including proofs-of-concept and total-cost-of-ownership analysis.</li> </ul> <h4>Communication and collaboration</h4> <ul> <li>Mentor junior and mid-level DevOps engineers through code reviews, pairing, and technical guidance.</li> <li>Partner with engineering, security, and product stakeholders to define technical requirements and influence platform direction.</li> <li>Communicate effectively with executive and technical audiences on cloud strategy, risk, and roadmap.</li> </ul> <h4>Education, experience & qualifications</h4> <ul> <li>Bachelor’s degree in computer science, information systems, or a related field.</li> <li>6+ years of hands-on experience in DevOps, cloud engineering, or SRE roles, including 3+ years with primary focus on Microsoft Azure (required).</li> <li>Expert-level Kubernetes administration, including cluster lifecycle management, upgrades, networking, and security hardening.</li> <li>Production experience operating Azure Kubernetes Service (AKS) at scale.</li> <li>Strong experience designing and maintaining infrastructure as code with Terraform, including module design and state management.</li> <li>Deep experience designing and operating CI/CD pipelines (e.g., GitHub Actions, Azure DevOps, GitLab CI).</li> <li>Hands-on experience with observability stacks (Prometheus, Grafana, Azure Monitor, ELK, or similar), including dashboard and alert design.</li> <li>Strong Linux system administration knowledge.</li> <li>Experience working with GitOps tools such as ArgoCD or Flux.</li> <li>Working knowledge of database administration in production (backups, performance tuning, HA/DR, and troubleshooting).</li> <li>Strong scripting and automation skills in Python, Bash, and/or PowerShell.</li> <li>Strong analytical and problem-solving abilities.</li> <li>Ability to collaborate effectively within cross-functional teams.</li> <li>Clear and precise documentation and communication skills.</li> <li>Fluency in both English and Arabic (spoken and written).</li> </ul> <h4>Behavioral competencies</h4> <ul> <li>Initiative</li> <li>Problem solving</li> <li>Team oriented</li> <li>Adaptability</li> <li>Ability to work under pressure</li> </ul> <h4>Technical competencies</h4> <ul> <li>Cloud computing fundamentals</li> <li>Linux operating systems</li> <li>Networking protocols and topologies</li> <li>Scripting and automation</li> <li>Monitoring and logging tools</li> <li>Security best practices</li> <li>System troubleshooting</li> <li>Backup and disaster recovery concepts</li> <li>Container orchestration (AKS / Kubernetes)</li> <li>GitOps (ArgoCD, Flux)</li> <li>Database administration</li> </ul></p><p></p>

<p><h4>Job purpose</h4> <p>As a senior DevOps engineer, you will own the design, reliability, and scalability of our cloud-based production systems. You will lead the architecture, automation, and operation of our Azure/AWS infrastructure, drive security, performance, and cost efficiency across our platforms, and set DevOps standards and best practices for the team. You will act as a technical authority on cloud operations, mentor junior engineers, and partner with engineering leadership to shape the platform roadmap.</p> <h4>Duties & responsibilities</h4> <h4>Cloud system operations</h4> <ul> <li>Lead the design, deployment, and operation of Azure/AWS cloud-based production systems.</li> <li>Own system performance, incident response, and root-cause analysis across production applications.</li> <li>Define release engineering and pre-production validation standards to ensure system quality and functionality.</li> <li>Architect and enforce backup, disaster recovery, and cost optimization (FinOps) strategies across cloud environments.</li> <li>Lead container orchestration and workload management on AKS/Kubernetes clusters, including upgrades, scaling, and hardening.</li> </ul> <h4>Automation and scripting</h4> <ul> <li>Design and maintain enterprise-grade automation frameworks for operational and platform processes.</li> <li>Build reusable tooling and scripts (e.g., Python, Bash, PowerShell) for automation, observability, and incident response.</li> <li>Lead GitOps adoption and continuous-delivery practices using ArgoCD or Flux.</li> </ul> <h4>Security and compliance</h4> <ul> <li>Define and enforce cloud security best practices, IAM policies, and secrets management across environments.</li> <li>Establish and maintain security protocols and compliance posture (e.g., ISO 27001, SOC 2 controls relevant to infrastructure).</li> </ul> <h4>Monitoring and metrics</h4> <ul> <li>Architect and operate observability platforms (metrics, logging, tracing) across Azure/AWS, defining SLOs, SLIs, and alerting strategy.</li> <li>Drive operational excellence by analyzing reliability metrics and leading post-incident reviews and improvement initiatives.</li> </ul> <h4>Research and evaluation</h4> <ul> <li>Evaluate and recommend emerging technologies, tools, and architectural patterns for adoption.</li> <li>Lead vendor and product evaluations, including proofs-of-concept and total-cost-of-ownership analysis.</li> </ul> <h4>Communication and collaboration</h4> <ul> <li>Mentor junior and mid-level DevOps engineers through code reviews, pairing, and technical guidance.</li> <li>Partner with engineering, security, and product stakeholders to define technical requirements and influence platform direction.</li> <li>Communicate effectively with executive and technical audiences on cloud strategy, risk, and roadmap.</li> </ul> <h4>Education, experience & qualifications</h4> <ul> <li>Bachelor’s degree in computer science, information systems, or a related field.</li> <li>6+ years of hands-on experience in DevOps, cloud engineering, or SRE roles, including 3+ years with primary focus on Microsoft Azure (required).</li> <li>Expert-level Kubernetes administration, including cluster lifecycle management, upgrades, networking, and security hardening.</li> <li>Production experience operating Azure Kubernetes Service (AKS) at scale.</li> <li>Strong experience designing and maintaining infrastructure as code with Terraform, including module design and state management.</li> <li>Deep experience designing and operating CI/CD pipelines (e.g., GitHub Actions, Azure DevOps, GitLab CI).</li> <li>Hands-on experience with observability stacks (Prometheus, Grafana, Azure Monitor, ELK, or similar), including dashboard and alert design.</li> <li>Strong Linux system administration knowledge.</li> <li>Experience working with GitOps tools such as ArgoCD or Flux.</li> <li>Working knowledge of database administration in production (backups, performance tuning, HA/DR, and troubleshooting).</li> <li>Strong scripting and automation skills in Python, Bash, and/or PowerShell.</li> <li>Strong analytical and problem-solving abilities.</li> <li>Ability to collaborate effectively within cross-functional teams.</li> <li>Clear and precise documentation and communication skills.</li> <li>Fluency in both English and Arabic (spoken and written).</li> </ul> <h4>Behavioral competencies</h4> <ul> <li>Initiative</li> <li>Problem solving</li> <li>Team oriented</li> <li>Adaptability</li> <li>Ability to work under pressure</li> </ul> <h4>Technical competencies</h4> <ul> <li>Cloud computing fundamentals</li> <li>Linux operating systems</li> <li>Networking protocols and topologies</li> <li>Scripting and automation</li> <li>Monitoring and logging tools</li> <li>Security best practices</li> <li>System troubleshooting</li> <li>Backup and disaster recovery concepts</li> <li>Container orchestration (AKS / Kubernetes)</li> <li>GitOps (ArgoCD, Flux)</li> <li>Database administration</li> </ul></p><p></p>

<p>1. JOB DETAILS:</p><p>Job Title: Analyst, ML Engineer</p><p>Reports to: Sr. Manager, IT Digital Platforms &amp; Digital Transformation</p><p>Department: IT Applications</p><p>Function: IT Applications</p><p>Company: Hikma Holding</p><p>2. JOB PURPOSE:</p><p>The ML Engineer is responsible for supporting the development, testing, and deployment of machine learning models and AI-powered pipelines across Hikma Pharmaceuticals. Working as part of the AI team under the Sr. Manager, IT Digital Platforms &amp; Digital Transformation, and under the close guidance of the AI Architect and AI developers team, this role provides hands-on ML engineering support across the solution lifecycle from data preparation and model experimentation through to deployment and monitoring. The ML Engineer is expected to develop their machine learning and data engineering skills rapidly within a structured team environment, contributing to Hikma's enterprise AI transformation while building foundational expertise in regulated pharmaceutical AI delivery.</p><p>3. JOB DIMENSIONS:</p><p>Number of Staff Supervised: Direct Reports Count: 0 (0 vacant)</p><p>Indirect Reports Count: N/A</p><p>Financial Budget (USD): Supports AI initiative delivery under team supervision</p><p>4. KEY ACCOUNTABILITIES:</p><p>ML Model Development &amp; Experimentation</p><ul><li>Support the development, training, and evaluation of machine learning models under the guidance of the AI Architect and senior team members, following approved architecture standards and initiative briefs</li><li>Assist in ML experimentation activities including data exploration, feature engineering, model selection, and performance evaluation using standard frameworks and cloud AI services</li><li>Apply foundational ML techniques across classical machine learning, NLP, and generative AI domains relevant to Hikma's business areas including Supply Chain, Quality, HR, and Commercial</li><li>Support the implementation of LLM-based solutions including RAG pipelines, prompt engineering, and embedding-based retrieval under senior technical guidance</li><li>Maintain experiment tracking logs, model versioning records, and reproducibility documentation using tools such as ML flow or Azure Machine Learning</li><li>Produce clear and accurate model development artefacts including experiment summaries, performance reports, and model documentation</li></ul><p>Data Engineering &amp; Feature Development</p><ul><li>Assist in building and maintaining ML data pipelines covering data ingestion, transformation, validation, and basic feature engineering for model training and inference workflows</li><li>Support data quality checks, anomaly detection, and dataset preparation activities to ensure ML model inputs meet required standards</li><li>Collaborate with the Data &amp; Analytics team to access and understand available data assets, following data governance and privacy guidelines</li><li>Work with structured data sources including relational databases and enterprise system extracts, developing proficiency in handling diverse data types over time</li></ul><p>ML-Ops &amp; Production Support</p><ul><li>Support the implementation and maintenance of ML-Ops pipeline components including model packaging, deployment, and basic performance monitoring under senior team guidance</li><li>Assist in deploying ML models to cloud environments using approved tooling (Azure Machine Learning, ML-flow, Docker, or equivalent)</li><li>Monitor deployed models for observable performance issues and flag anomalies to the AI Architect or senior team members for investigation</li><li>Maintain accurate records in model registries including versioning and change logs across AI initiatives</li><li>Contribute to the documentation and validation support activities for ML models deployed in GxP-regulated contexts, following defined compliance processes</li></ul><p>Quality Assurance &amp; Testing</p><ul><li>Support the development and execution of testing activities for ML solutions, including data, pipeline tests, model performance checks, and basic integration testing</li><li>Actively participate in code reviews and technical walkthroughs, applying feedback to improve code quality and engineering practices</li><li>Document assigned ML components clearly including data preparation steps, model configurations, test results, and known issues</li><li>Identify and escalate technical issues encountered across the ML stack in a timely and structured manner</li></ul><p>5. Behavioural Competencies:</p><p>Initiative &amp; Drive for Results - Very Good</p><p>Change &amp; Innovation - Very Good</p><p>Communication &amp; Influence - Good</p><p>Developing &amp; Empowering others - Good</p><p>Problem Solving &amp; decision Making - Excellent</p><p>Strategic Thinking - Good</p><p>6. Technical Competencies:</p><p>ML Model Development &amp; Experimentation - Good</p><p>Python &amp; ML Frameworks (PyTorch, TensorFlow, scikit-learn, HuggingFace) - Good</p><p>MLOps &amp; Model Lifecycle Tooling - Good</p><p>Data Engineering &amp; Pipeline Support - Good</p><p>Cloud AI/ML Services (Azure ML) - Good</p><p>Generative AI &amp; LLM Development (RAG, LangChain, Semantic Kernel) - Very Good</p><p>Model Explainability &amp; Responsible AI Awareness - Very Good</p><p>Containerization &amp; CI/CD Basics (Docker, Git, Azure DevOps) - Good</p><p>7. COMMUNICATIONS &amp; WORKING RELATIONSHIPS:</p><p>Internal: AI Champions Network, AI Architect, AI Developer, Data &amp; Analytics Team</p><p>External: AI/ML Vendors &amp; Solution Providers, Implementation Partners, Open Source &amp; Developer Communities</p><p><strong>Desired Candidate Profile</strong></p><ul><li>Bachelor's degree in computer science, Software Engineering, Data Science, Mathematics, Statistics, or related technical field *(Required)*</li><li>Master's degree in Artificial Intelligence, Machine Learning, Data Science, or Computer Science *(Preferred)*</li><li>Microsoft Azure AI Engineer Associate, AWS Machine Learning Specialty, or Google Professional ML Engineer certification *(Preferred)*</li><li>Relevant ML/AI certifications (e.g., DeepLearning.AI ML Specialization, Databricks ML Associate, fast.ai) *(Preferred)*</li><li>0-1 year of professional experience in software development, data science, machine learning, or a related technical field (fresh graduates are welcome to apply)</li><li>Demonstrated hands-on experience with ML model development through academic projects, internships, capstone projects, hackathons, or personal projects</li><li>Familiarity with cloud AI/ML platforms (Azure, AWS, or GCP) gained through coursework, self-study, or practical experimentation *(Preferred)*</li><li>Any exposure to generative AI, LLM tools, or RAG concepts through academic or personal projects *(Preferred)*</li><li>Pharmaceutical, healthcare, life sciences, or other regulated industry exposure *(Preferred but not expected)*</li><li>Working knowledge of Python and foundational ML/DL libraries including scikit-learn, pandas, NumPy, and at least one deep learning framework</li><li>Practical exposure to training and evaluating ML models including classical machine learning and at least one of: NLP, computer vision, or time-series analysis through academic, personal, or internship projects</li><li>Basic familiarity with generative AI concepts including Large Language Models, prompt engineering, and RAG principles *(Preferred)*</li><li>Awareness of ML-Ops concepts and tools such as ML-flow or Azure Machine Learning for experiment tracking and model management *(Preferred)*</li><li>Basic experience with data preparation, cleaning, transformation, and exploratory data analysis using Python</li><li>Familiarity with version control using Git and basic software development practices including code reviews and testing</li><li>Basic understanding of cloud platforms (Azure, AWS, or GCP) and awareness of cloud-based AI/ML services *(Preferred)*</li><li>Familiarity with containerization concepts (Docker) and CI/CD basics *(Preferred)*</li><li>Awareness of responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security practices relevant to ML development</li><li>Any familiarity with pharmaceutical business processes or GxP compliance in a technology context is a plus *(Preferred)*</li></ul>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>We are looking for a highly skilled L2 Oracle KVM Virtualization Engineer with strong hands-on experience in Oracle Linux KVM and libvirt within enterprise production environments.<br> The role will be responsible for managing KVM hypervisors, virtual machines, HA configurations, live migration, storage and network integration, performance optimization, patching, backup/DR, and production incident management .<br> The ideal candidate should have strong troubleshooting skills and experience supporting mission-critical infrastructure in 24x7 production and DR environments .<br> Key Responsibilities Install, configure, administer, and maintain Oracle Linux KVM hypervisors .<br> Manage the complete VM lifecycle , including provisioning, configuration, migration, and decommissioning.<br> Configure and support High Availability (HA) and live migration .<br> Manage storage integration with KVM hosts, including iSCSI, NFS, SAN, and FC .<br> Configure and troubleshoot virtual networking, including VLANs, bridges, and bonding .<br> Monitor virtualization platform health, performance, capacity, and availability.<br> Perform CPU, memory, and disk I/O performance tuning .<br> Apply hypervisor patches, Linux kernel updates, and security hardening.<br> Manage VM backup and restore activities in coordination with backup teams.<br> Support DR environments and VM replication strategies.<br> Troubleshoot host failures, VM crashes, resource contention, and virtualization performance issues.<br> Perform Root Cause Analysis (RCA) for production incidents.<br> Support infrastructure upgrades, migrations, and technology refresh activities.<br> Coordinate with Linux/OS, Storage, Network, Backup, and Cloud teams .<br> Maintain technical documentation and operational procedures.<br> Follow ITIL-based Incident, Problem, and Change Management processes.<br> Provide L2 support within a 24x7 managed services environment 8–12 years of experience in virtualization and Linux administration.<br> Strong hands-on experience with Oracle Linux KVM .<br> Strong knowledge of KVM and libvirt .<br> Experience managing enterprise production virtualization environments .<br> Hands-on experience with HA and live migration .<br> Strong knowledge of Oracle Linux / RHEL .<br> Experience supporting Production and DR environments .<br> Strong understanding of virtualization architecture and resource allocation.<br> Hands-on experience with: iSCSI NFS SAN Fibre Channel (FC) Strong understanding of virtual networking: VLAN Linux Bridges Bonding Experience with virtualization performance tuning and troubleshooting.<br> Experience with patching, upgrades, security hardening, and vulnerability remediation .<br> Basic scripting knowledge using Shell and/or Python .<br> Strong troubleshooting, analytical, and incident-management skills.<br> Familiarity with ITIL Incident, Problem, and Change Management .<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 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 \u2192 Pass qualification(s) \u2192 Join a project \u2192 Complete tasks \u2192 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>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>We are seeking a Senior AI Software Engineer to join our AI Hub, where we rapidly deliver prototypes, proof-of-concepts (PoCs), and minimum viable products (MVPs), as well as delivering production grade solutions that bring AI use cases to life. This role demands expertise across both front-end and back-end development, supporting the creation of dynamic AI-driven products such as GenAI-enabled web applications, use case marketplaces, and diagnostic tools. You will be responsible for designing, building, and optimizing end-to-end solutions, translating business requirements into robust, scalable, secure, and accessible web experiences suitable for enterprise production environments.</p></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><ul><li>5+ years of professional experience delivering enterprise-grade software solutions across backend and frontend systems.</li><li>Strong hands-on experience building secure, scalable, and maintainable APIs using Python and Node.js, with frameworks such as FastAPI and Fastify.</li><li>Solid frontend engineering experience with TypeScript, React, and Next.js, following enterprise UI and accessibility standards.</li><li>Proven experience containerizing, deploying, and operating applications in production using Docker and Kubernetes, with a strong understanding of reliability and scalability.</li><li>Experience designing, implementing, and maintaining CI/CD pipelines using Jenkins, GitHub Actions, or Azure DevOps, with automated testing and quality gates.</li><li>Hands-on experience deploying and operating production workloads on Microsoft Azure, including services such as AKS, Azure Container Apps, Azure Container Registry, and related platform services.</li><li>Strong understanding and application of software engineering best practices, including SOLID principles, clean architecture, and common enterprise design patterns.</li><li>Experience working with relational databases such as MS SQL Server and PostgreSQL, including schema design, query optimization, and transactional consistency.</li><li>Practical experience with vector databases (e.g., Chroma, Milvus, Pinecone) and pgvector, including building and operating Retrieval Augmented Generation (RAG) pipelines in production environments.</li><li>Ability to collaborate effectively in cross-functional enterprise teams, including product, architecture, security, and cloud/platform teams.</li><li>Experience working with LLMs and AI platforms, such as Azure OpenAI, OpenAI APIs, or similar enterprise AI services.</li><li>Familiarity with LLM orchestration frameworks such as LangChain, Semantic Kernel, or equivalent.</li><li>hands-on experience using AI coding assistants (Claude Code, OpenAI Codex,..etc)</li><li>Strong documentation skills and experience contributing to architecture decisions, technical standards, and internal best practices.</li><li>Prior experience mentoring junior engineers or contributing to engineering excellence initiatives.</li></ul><p></p></section>

<p>Description About the Opportunity We are hiring on behalf of a leading government transformation initiative in Abu Dhabi that is building one of the world’s most ambitious AI programs. This organisation is creating the infrastructure, platforms, and products that will enable government entities to operate as AI-native institutions. The work spans sovereign AI infrastructure, event-driven systems, and production-grade applications that support critical public services. This is an opportunity to work on highly impactful systems where reliability, scalability, and security are essential. Role Overview We are looking for a Senior Backend Engineer to design, build, and operate the backend platform that powers advanced AI and data products. You will own core services, APIs, event pipelines, and data infrastructure that support production systems at scale. This role is backend-focused and requires deep expertise in distributed systems, cloud infrastructure, Kubernetes, and database reliability. While you do not need to be an AI specialist, you should be comfortable building the platform foundations that modern AI applications depend on. At the senior level, you will: Architect and own major backend subsystems end-to-end Lead reliability and operational excellence initiatives Mentor other engineers and contribute to technical standards Collaborate with senior technical leadership to shape platform direction Use AI coding tools such as Codex, Claude Code, or similar in your daily workflow Key Responsibilities Backend Services & Event-Driven Architecture Design, build, and operate scalable backend services and APIs Develop event-driven systems using Kafka, Azure Service Bus, or Azure Event Hubs Build data pipelines, ETL processes, and shared platform abstractions Manage PostgreSQL, caching, and object storage infrastructure Cloud Infrastructure & Reliability Deploy and operate containerized services on Kubernetes Build CI/CD pipelines and infrastructure-as-code using Terraform Manage cloud infrastructure on Azure Define and monitor SLOs, metrics, logging, tracing, and alerting Lead incident response, root-cause analysis, and postmortems Security, Standards & Mentorship Implement secure engineering practices and compliance requirements Contribute to IAM, secrets management, network policy, and zero-trust architecture Mentor engineers through design reviews and code reviews Basic Qualifications 7+ years of backend or platform engineering experience Strong expertise in distributed systems and event-driven architecture Proficiency in Python, Java/Kotlin, or Go Hands-on experience with Azure (AWS or GCP experience also valued) Production experience with Docker and Kubernetes Strong knowledge of PostgreSQL at scale Experience with observability, SLOs, and incident response Practical use of AI coding assistants such as Codex or Claude Code Excellent written and verbal communication skills Preferred Qualifications Experience building internal developer platforms and shared tooling Service mesh experience (Istio, Linkerd) Familiarity with CQRS, event sourcing, sagas, and outbox patterns Experience with Redis, Elasticsearch, or OpenSearch Exposure to AI infrastructure such as retrieval pipelines and model serving Security experience in regulated environments Experience working in government, defense, financial services, or similar sectors Technology Stack  Languages: Python, Java, Kotlin, Go  APIs: REST, gRPC, WebSockets, SSE  Event Streaming: Kafka, Azure Service Bus, Azure Event Hubs  Data: PostgreSQL, Redis, DocumentDB, Azure Blob Storage  Infrastructure: Azure, Docker, Kubernetes, Terraform  Observability: Prometheus, Grafana, OpenTelemetry  Security: Entra ID, RBAC, Secrets Management, Zero Trust  AI Development: Codex, Claude Code, OpenCode What We’re Looking For You are an engineer who: Takes full ownership from design to production operations Builds reliable, scalable systems with strong operational discipline Makes evidence-based technical decisions Mentors others and raises the engineering bar Communicates clearly and proactively Embraces AI-native engineering practices Why Apply? This role offers the opportunity to work on highly consequential systems that will shape the future of AI-powered public services. You’ll join a world-class engineering environment tackling complex challenges in backend infrastructure, distributed systems, and cloud architecture—while delivering meaningful impact at a national scale.</p><p></p>

<p>Description About the Opportunity We are hiring on behalf of a leading government transformation initiative in Abu Dhabi that is building one of the world’s most ambitious AI programs. This organisation is creating the infrastructure, platforms, and products that will enable government entities to operate as AI-native institutions. The work spans sovereign AI infrastructure, event-driven systems, and production-grade applications that support critical public services. This is an opportunity to work on highly impactful systems where reliability, scalability, and security are essential. Role Overview We are looking for a Senior Backend Engineer to design, build, and operate the backend platform that powers advanced AI and data products. You will own core services, APIs, event pipelines, and data infrastructure that support production systems at scale. This role is backend-focused and requires deep expertise in distributed systems, cloud infrastructure, Kubernetes, and database reliability. While you do not need to be an AI specialist, you should be comfortable building the platform foundations that modern AI applications depend on. At the senior level, you will: Architect and own major backend subsystems end-to-end Lead reliability and operational excellence initiatives Mentor other engineers and contribute to technical standards Collaborate with senior technical leadership to shape platform direction Use AI coding tools such as Codex, Claude Code, or similar in your daily workflow Key Responsibilities Backend Services & Event-Driven Architecture Design, build, and operate scalable backend services and APIs Develop event-driven systems using Kafka, Azure Service Bus, or Azure Event Hubs Build data pipelines, ETL processes, and shared platform abstractions Manage PostgreSQL, caching, and object storage infrastructure Cloud Infrastructure & Reliability Deploy and operate containerized services on Kubernetes Build CI/CD pipelines and infrastructure-as-code using Terraform Manage cloud infrastructure on Azure Define and monitor SLOs, metrics, logging, tracing, and alerting Lead incident response, root-cause analysis, and postmortems Security, Standards & Mentorship Implement secure engineering practices and compliance requirements Contribute to IAM, secrets management, network policy, and zero-trust architecture Mentor engineers through design reviews and code reviews Basic Qualifications 7+ years of backend or platform engineering experience Strong expertise in distributed systems and event-driven architecture Proficiency in Python, Java/Kotlin, or Go Hands-on experience with Azure (AWS or GCP experience also valued) Production experience with Docker and Kubernetes Strong knowledge of PostgreSQL at scale Experience with observability, SLOs, and incident response Practical use of AI coding assistants such as Codex or Claude Code Excellent written and verbal communication skills Preferred Qualifications Experience building internal developer platforms and shared tooling Service mesh experience (Istio, Linkerd) Familiarity with CQRS, event sourcing, sagas, and outbox patterns Experience with Redis, Elasticsearch, or OpenSearch Exposure to AI infrastructure such as retrieval pipelines and model serving Security experience in regulated environments Experience working in government, defense, financial services, or similar sectors Technology Stack  Languages: Python, Java, Kotlin, Go  APIs: REST, gRPC, WebSockets, SSE  Event Streaming: Kafka, Azure Service Bus, Azure Event Hubs  Data: PostgreSQL, Redis, DocumentDB, Azure Blob Storage  Infrastructure: Azure, Docker, Kubernetes, Terraform  Observability: Prometheus, Grafana, OpenTelemetry  Security: Entra ID, RBAC, Secrets Management, Zero Trust  AI Development: Codex, Claude Code, OpenCode What We’re Looking For You are an engineer who: Takes full ownership from design to production operations Builds reliable, scalable systems with strong operational discipline Makes evidence-based technical decisions Mentors others and raises the engineering bar Communicates clearly and proactively Embraces AI-native engineering practices Why Apply? This role offers the opportunity to work on highly consequential systems that will shape the future of AI-powered public services. You’ll join a world-class engineering environment tackling complex challenges in backend infrastructure, distributed systems, and cloud architecture—while delivering meaningful impact at a national scale.</p><p></p>

<p><br></p><p>Scope of the Position The Systems Administrator is responsible for the effective provisioning, installation, configuration, operation, and maintenance of systems hardware, software, and related infrastructure in both Windows and Linux environments. This role ensures that IT systems run efficiently and securely, serving the needs of the organization. The candidate will work with various teams to design and implement solutions that enhance IT service delivery.</p><p>Primary Duties and Responsibilities</p><p>System Administration (Windows &amp; Linux):</p><ul><li>Manage and maintain both Windows Server and Linux environments, ensuring high availability, security, and performance.</li><li>Install, configure, and update server operating systems, applications, and related infrastructure.</li><li>Monitor server health, performance, and security to identify and resolve potential issues.</li><li>Perform system backups, restore operations, and disaster recovery planning.</li><li>Manage user accounts, permissions, and access control on both Windows and Linux systems.</li></ul><p>Security &amp; Compliance:</p><ul><li>Implement and maintain security protocols, patch management, and vulnerability assessments for both environments.</li><li>Ensure systems comply with relevant industry standards and regulations (e.g., SOC2, PCI-DSS , GDPR).</li><li>Administer Active Directory (AD) and domain security policies on Windows systems.</li></ul><p>Networking &amp; Infrastructure:</p><ul><li>Collaborate with network and security teams to optimize system integrations and improve IT infrastructure resilience.</li></ul><p>Automation &amp; Scripting:</p><ul><li>Develop and maintain automation scripts for routine tasks using PowerShell, Bash, Python, or other scripting languages.</li><li>Automate system deployments, monitoring, and updates for both Windows and Linux environments.</li></ul><p>Virtualization &amp; Cloud:</p><ul><li>Manage virtualization platforms such as VMware, Hyper-V, or other hypervisors.</li><li>Support cloud infrastructure (e.g., AWS, Azure) as part of a hybrid environment, managing servers and services deployed in the cloud.</li><li>Migrate workloads between on-premises and cloud platforms when needed.</li></ul><p>Technical Support &amp; Troubleshooting:</p><ul><li>Provide advanced troubleshooting support for system issues, ensuring timely resolution of critical incidents.</li><li>Collaborate with helpdesk and other IT staff to provide technical assistance and training to end-users.</li><li>Document system configurations, procedures, and troubleshooting guides for future reference.</li></ul><p><strong>Desired Candidate Profile</strong></p><ul><li>Bachelor's degree in computer science, Information Technology, or a related field (or equivalent work experience).</li><li>3-5 years of experience as a Systems Administrator or similar role, with experience managing both Windows and Linux environments.</li><li>Strong knowledge of Windows Server (Active Directory, Group Policy, DNS, DHCP) and Linux distributions (Red Hat, Ubuntu, CentOS).</li><li>Proficiency with scripting and automation using PowerShell, Bash, or Python.</li><li>Experience with virtualization technologies (e.g., VMware, Hyper-V).</li><li>Familiarity with cloud environments (AWS, Azure, or GCP).</li><li>Solid understanding of networking concepts and protocols (e.g., TCP/IP, DNS, VPN).</li><li>Relevant certifications are a plus (e.g., Microsoft Certified: Windows Server, CompTIA Linux+, Red Hat Certified System Administrator, AWS Certified SysOps Administrator).</li><li>Working at a desk on a computer for long periods of time.</li><li>Lifting heavy equipment, such as servers.</li><li>Requires occasional on-call support for critical system incidents and maintenance during off-hours.</li></ul>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>Do you want to love what you do at work? Do you want to make a difference, an impact, transform peoples lives? Do you want to work with a team that believes in disrupting the normal, boring, and average? If yes, then this is the job you're looking for , webook.com is Saudi's #1 event ticketing and experience booking platforms in terms of technology, features, agility, revenue serving some of the largest mega events in the Kingdom surpassing over 2 billion sales.</p><p>Key Responsibilities:</p><ul><li><strong>Data Integration and ETL Development</strong>: Architect and implement robust data integration pipelines to extract, transform, and load data from various sources (e.g., databases, SaaS applications, APIs, and flat files) into a centralized data platform. Design and develop complex ETL (Extract, Transform, Load) processes to ensure data quality, consistency, and reliability. Optimize data transformation workflows to improve performance and scalability.</li><li><strong>Data Infrastructure and Platform Management</strong>: Implement and maintain data ingestion, processing, and storage solutions to support the organization's data and analytics requirements. Ensure the reliability, security, and availability of the data infrastructure through effective monitoring, troubleshooting, and disaster recovery planning.</li><li><strong>Data Governance and Metadata Management</strong>: Collaborate with the data governance team to establish data policies, standards, and procedures. Develop and maintain a comprehensive metadata management system to ensure data lineage, provenance, and traceability. Implement data quality control measures and data validation processes to ensure the integrity and reliability of the data.</li></ul></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>5-6 years of experience as a Data Engineer or a related role in a data-driven organization. Proficient in designing and implementing data integration and ETL pipelines using tools such as Apache Airflow, airbyte, or any cloud-based data integration services. Strong experience in setting up and managing data infrastructure, including data lakes, data warehouses, and real-time streaming platforms (e.g. Elastic , Google Bigquery, Mongodb). Expertise in data modeling, data quality management, and metadata management. Proficient in programming languages such as Python, or Java, and experience with SQL. Familiarity with cloud computing platforms (e.g., AWS,Google Cloud) and DevOps practices. Excellent problem-solving skills and the ability to work collaboratively with cross-functional teams. Strong communication and presentation skills to effectively translate technical concepts to business stakeholders.</p><p>Preferred Qualifications:</p><ul><li>Familiarity with data visualization and business intelligence tools (e.g., Tableau, qlik.etc).</li><li>Knowledge of machine learning and artificial intelligence concepts and their application in data-driven initiatives.</li><li>Project management experience and the ability to lead data integration and infrastructure initiatives.</li></ul><p>If you are a seasoned Data Engineer with a passion for building scalable and robust data integration solutions, we encourage you to apply for this exciting opportunity</p><p></p></section>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>The Systems Administrator is responsible for the effective provisioning, installation, configuration, operation, and maintenance of systems hardware, software, and related infrastructure in both Windows and Linux environments. This role ensures that IT systems run efficiently and securely, serving the needs of the organization. The candidate will work with various teams to design and implement solutions that enhance IT service delivery.</p><p><strong>Primary Duties and Responsibilities</strong></p><p><strong>System Administration (Windows &amp; Linux):</strong></p><ul><li>Manage and maintain both Windows Server and Linux environments, ensuring high availability, security, and performance.</li><li>Install, configure, and update server operating systems, applications, and related infrastructure.</li><li>Monitor server health, performance, and security to identify and resolve potential issues.</li><li>Perform system backups, restore operations, and disaster recovery planning.</li><li>Manage user accounts, permissions, and access control on both Windows and Linux systems.</li></ul><p><strong>Security &amp; Compliance:</strong></p><ul><li>Implement and maintain security protocols, patch management, and vulnerability assessments for both environments.</li><li>Ensure systems comply with relevant industry standards and regulations (e.g., SOC2, PCI-DSS , GDPR).</li><li>Administer Active Directory (AD) and domain security policies on Windows systems.</li></ul><p><strong>Networking &amp; Infrastructure:</strong></p><ul><li>Collaborate with network and security teams to optimize system integrations and improve IT infrastructure resilience.</li></ul><p><strong>Automation &amp; Scripting:</strong></p><ul><li>Develop and maintain automation scripts for routine tasks using PowerShell, Bash, Python, or other scripting languages.</li><li>Automate system deployments, monitoring, and updates for both Windows and Linux environments.</li></ul><p><strong>Virtualization &amp; Cloud:</strong></p><ul><li>Manage virtualization platforms such as VMware, Hyper-V, or other hypervisors.</li><li>Support cloud infrastructure (e.g., AWS, Azure) as part of a hybrid environment, managing servers and services deployed in the cloud.</li><li>Migrate workloads between on-premises and cloud platforms when needed.</li></ul><p><strong>Technical Support &amp; Troubleshooting:</strong></p><ul><li>Provide advanced troubleshooting support for system issues, ensuring timely resolution of critical incidents.</li><li>Collaborate with helpdesk and other IT staff to provide technical assistance and training to end-users.</li><li>Document system configurations, procedures, and troubleshooting guides for future reference.</li></ul></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><ul><li>Bachelor's degree in computer science, Information Technology, or a related field (or equivalent work experience).</li><li>3-5 years of experience as a Systems Administrator or similar role, with experience managing both Windows and Linux environments.</li><li>Strong knowledge of Windows Server (Active Directory, Group Policy, DNS, DHCP) and Linux distributions (Red Hat, Ubuntu, CentOS).</li><li>Proficiency with scripting and automation using PowerShell, Bash, or Python.</li><li>Experience with virtualization technologies (e.g., VMware, Hyper-V).</li><li>Familiarity with cloud environments (AWS, Azure, or GCP).</li><li>Solid understanding of networking concepts and protocols (e.g., TCP/IP, DNS, VPN).</li><li>Relevant certifications are a plus (e.g., Microsoft Certified: Windows Server, CompTIA Linux+, Red Hat Certified System Administrator, AWS Certified SysOps Administrator).</li><li>Requires occasional on-call support for critical system incidents and maintenance during off-hours.</li></ul><p></p></section>

<p>Job Summary : The Senior Network Design &amp; Implementation Engineer is responsible for architecting, designing, deploying and testing end-to-end enterprise network, edge security and SD-WAN solutions centered around the Fortinet Security Fabric . This role requires advanced knowledge and experience in FortiGate Secure SD-WAN with advanced next-generation firewalling (NGFW), zero-trust architecture, and cloud connectivity. This position will also require familiarity with other SD-WAN solutions such as Arista Velocloud and Cisco SD-WAN. As a senior technical lead, this position converts complex enterprise connectivity and compliance requirements into validated Low-Level Designs (LLDs), automated configuration templates, structured migration cutover plans, and seamless operational handoffs.</p><p>Key Responsibilities :</p><p>1. Architecture &amp; Low-Level Design (LLD)</p><ul><li>Secure SD-WAN Design: Architect scalable FortiGate Secure SD-WAN topologies across multi-region environments, including Hub-and-Spoke, Full Mesh, and Dynamic Mesh VPN (ADVPN) structures with automated BGP routing.</li><li>SLA &amp; Steering Profiles: Design SD-WAN rules, Performance SLAs, link health monitoring probes, and application control steering policies using Dynamic Application Steering to optimize voice, SaaS, and critical business traffic over diverse underlays (DIA, MPLS, 5G/LTE).</li><li>Fortinet Security Fabric Integration: Build comprehensive edge and core security layouts incorporating FortiGate NGFWs , FortiManager , FortiAnalyzer , FortiSwitch , and FortiAP (FortiLAN Cloud/Fabric extensions).</li><li>Zero Trust &amp; Edge Security: Design and integrate Zero Trust Network Access (ZTNA), FortiSASE, SSL/TLS deep inspection, IPS, Web/URL filtering, and inline malware prevention profiles across local and remote access tiers.</li><li>Underlay &amp; Overlay Integration: Standardize complex BGP (iBGP/eBGP), OSPF, VRF, NAT, and IPsec tunnel architectures integrating SD-WAN overlays with existing enterprise data centers, core switching backbones, and legacy WANs.</li><li>Design Documentation: Develop complete High-Level Designs (HLDs), Low-Level Designs (LLDs), IP/VLAN schemes, network topology diagrams (Visio/Lucidchart/draw.io), and Bill of Materials (BOMs).</li></ul><p>2. Staging, Migration, &amp; Hands-On Implementation</p><ul><li>Deployment Engineering: Configure, stage, and deploy physical and virtual FortiGate appliances utilizing FortiManager for centralized policy management, device mapping, and revision tracking.</li><li>Migration Execution: Author detailed Method of Procedure (MOP) documents, cutover playbooks, rollback strategies, and maintenance window schedules for migrating legacy firewalls and WAN circuits to Fortinet Secure SD-WAN.</li><li>Hybrid &amp; Multi-Cloud Connectivity: Implement secure cloud edge connectivity, deploying virtual FortiGate instances (FortiGate-VM) in Azure using Cloud On-Ramp methodologies and Transit Gateways.</li><li>LAN/WLAN Integration: Configure FortiSwitch and FortiAP deployments using FortiLink to deliver unified, single-pane-of-glass management through FortiGate controllers.</li></ul><p>3. Automation &amp; Deployment Standardization</p><ul><li>Template Standardization: Build and maintain standardized CLI scripts, FortiManager Meta-Fields, Normalized Interfaces, and Jinja2 templates to enable zero-touch provisioning (ZTP) for global site rollouts.</li><li>Infrastructure as Code (IaC): Automate policy pushes, object creation, and compliance checks using Python, Ansible, or Terraform (FortiOS/FortiManager providers).</li></ul><p>4. Validation, Operational Handoff, &amp; Escalation</p><ul><li>Acceptance &amp; Failover Testing: Execute rigorous post-implementation testing (failover validation, link brownout/blackout simulations, throughput benchmarking, and HA cluster state verification).</li><li>Operational Enablement: Author Standard Operating Procedures (SOPs), knowledge-base articles, and operational runbooks; conduct formal training and handoff sessions for Tier-2/3 operations and NOC teams.</li><li>Tier-4 Escalation: Serve as the ultimate technical authority during complex cutovers, critical outage recoveries, and escalated Fortinet TAC engagements.</li></ul><p><strong>Desired Candidate Profile</strong></p><ul><li><b>Technical Expertise :</b></li><li>Fortinet Ecosystem: 6+ years of hands-on architectural and implementation experience with Fortinet solutions, including FortiGate (FortiOS), FortiManager, FortiAnalyzer, FortiSwitch, FortiAP, and FortiClient.</li><li>Fortinet Secure SD-WAN: Deep expertise in ADVPN, SD-WAN rules, Performance SLAs, BGP over IPsec overlays, and centralized orchestration via FortiManager.</li><li>Routing &amp; Networking Protocols: Expert-level understanding of BGP (path selection, communities, route reflection), OSPF, VRFs, VXLAN, EVPN, IPsec, NAT, IPv6, and QoS models.</li><li>Network Automation: Practical proficiency in scripting and automation using Python, Ansible, Terraform, or REST APIs.</li><li>Arista Velocloud: Familiarity with Arista Velocloud solutions in order to integrate with, and eventually migrate off of the current network based on Velocloud.</li><li><b>Education &amp; Experience :</b></li><li>Bachelor s degree in Computer Science, Network Engineering, Information Technology, or equivalent industry experience.</li><li>8+ years of experience in enterprise network engineering, with a focus on enterprise security design and multi-site WAN transformations to SD-WAN.</li><li><b>Preferred Certifications :</b></li><li>Fortinet: Fortinet Certified Solution Specialist (FCSS) in Network Security or Secure SD-WAN; Fortinet Certified Expert (FCX) / NSE 8 highly valued.</li><li>Industry Certifications: CCIE (Enterprise Infrastructure or Security), CCNP Enterprise/Security, or equivalent expert-level network engineering credentials.</li></ul>

<p><h4>Description</h4> <p>About the opportunity<br> We are hiring on behalf of a major government transformation initiative in Abu Dhabi that is building one of the world’s most ambitious applied AI programs. This organisation is developing the infrastructure and platforms that enable AI-powered public services at scale. The work spans sovereign model serving, vector infrastructure, retrieval systems, developer tooling, and deployment platforms that support critical government applications. This is a rare opportunity to build foundational AI infrastructure with significant real-world impact, in an environment where performance, reliability, and security are mission-critical.</p> <h4>Role overview</h4> <p>We are seeking a Staff AI Platform Engineer to own the platform infrastructure that enables engineering teams to build and operate AI systems efficiently and reliably. This is a senior individual contributor role focused on model serving, vector infrastructure, data pipelines, observability, and deployment platforms. Your success will be measured by the productivity and reliability gains you enable for other engineers.</p> <p>You will combine deep platform engineering expertise with a strong understanding of AI workload requirements, including inference performance, retrieval systems, and operational complexity.</p> <p>At the staff level, you will:</p> <ul> <li>Define and evolve the technical foundations of the AI platform</li> <li>Build systems that accelerate engineering teams across the organisation</li> <li>Set standards for reliability, security, and scalability</li> <li>Lead architecture decisions and evaluate emerging technologies</li> <li>Use AI coding tools such as Codex, Claude Code, or similar as part of your daily workflow</li> </ul> <h4>Key responsibilities</h4> <p><strong>AI platform infrastructure</strong><br> Design and operate model serving infrastructure using vLLM, TGI, TensorRT-LLM, or similar technologies.<br> Build and manage vector infrastructure, embedding pipelines, and retrieval systems.<br> Develop data pipelines for document ingestion, transformation, and storage.<br> Own deployment platforms, CI/CD pipelines, and infrastructure-as-code.</p> <p><strong>Observability & reliability</strong><br> Build observability across the AI stack, including latency, throughput, and model behavior monitoring.<br> Define SLOs, perform capacity planning, and lead reliability engineering initiatives.<br> Lead incident response, root-cause analysis, and postmortems.<br> Establish platform standards for operational excellence.</p> <p><strong>Force multiplication</strong><br> Build reusable internal tooling, SDKs, and deployment patterns.<br> Partner with engineering teams to translate infrastructure needs into scalable platform capabilities.<br> Mentor engineers and raise technical standards across the organisation.</p> <h4>Basic qualifications</h4> <ul> <li>10+ years of platform, infrastructure, or backend engineering experience</li> <li>Proven experience operating at staff or principal engineer level</li> <li>Deep expertise in Azure (AWS or GCP also valued)</li> <li>Strong Kubernetes and Docker experience</li> <li>Hands-on experience with Terraform, CI/CD pipelines, and infrastructure-as-code</li> <li>Experience building production data pipelines</li> <li>Proficiency in Python, Java/Kotlin, or Go</li> <li>Strong PostgreSQL knowledge at production scale</li> <li>Experience with distributed tracing, metrics, logging, and alerting</li> <li>Practical use of AI coding assistants such as Codex or Claude Code</li> <li>Excellent written and verbal communication skills</li> </ul> <h4>Preferred qualifications</h4> <ul> <li>Experience building RAG systems and retrieval infrastructure</li> <li>Experience with LangGraph, LangChain, Semantic Kernel, or similar frameworks</li> <li>Hands-on experience with GPU-based inference infrastructure</li> <li>Experience with vector databases such as pgvector, Qdrant, Pinecone, or Weaviate</li> <li>Familiarity with LLM inference optimization and evaluation frameworks</li> <li>Experience with speech and conversational AI systems</li> <li>Experience building internal developer platforms</li> <li>Security and compliance experience in highly regulated environments</li> </ul> <h4>Technology stack</h4> <p><strong>Backend & platform:</strong> Python, Java, Kotlin, Go, REST, gRPC<br> <strong>AI infrastructure:</strong> vLLM, TGI, TensorRT-LLM, GPU Serving<br> <strong>AI & LLM:</strong> LangChain, LangGraph, Microsoft Agent Framework, vector databases<br> <strong>Data:</strong> PostgreSQL, Redis, DocumentDB, Azure Blob Storage<br> <strong>Infrastructure:</strong> Azure, Docker, Kubernetes, Terraform, CI/CD<br> <strong>Observability:</strong> Langfuse, Grafana, Prometheus, OpenTelemetry<br> <strong>Security:</strong> Entra ID, RBAC, Secrets Management, Zero Trust<br> <strong>AI development:</strong> Codex, Claude Code, OpenCode</p> <h4>What we’re looking for</h4> <p>You are an engineer who:</p> <ul> <li>Thinks in systems and enables others to move faster</li> <li>Designs infrastructure that is reliable, scalable, and easy to operate</li> <li>Grounds technical decisions in evidence and operational data</li> <li>Raises engineering standards through architecture and mentorship</li> <li>Communicates clearly and transparently</li> <li>Embraces AI-native development practices</li> </ul> <h4>Why apply?</h4> <p>This role offers the opportunity to shape the technical foundations of a world-class AI platform supporting transformative public services in Abu Dhabi. You will tackle complex infrastructure challenges at the intersection of AI, cloud computing, and platform engineering while delivering meaningful impact at a national scale.</p></p><p></p>

<p><h4>Description</h4> <p>About the opportunity<br> We are hiring on behalf of a major government transformation initiative in Abu Dhabi that is building one of the world’s most ambitious applied AI programs. This organisation is developing the infrastructure and platforms that enable AI-powered public services at scale. The work spans sovereign model serving, vector infrastructure, retrieval systems, developer tooling, and deployment platforms that support critical government applications. This is a rare opportunity to build foundational AI infrastructure with significant real-world impact, in an environment where performance, reliability, and security are mission-critical.</p> <h4>Role overview</h4> <p>We are seeking a Staff AI Platform Engineer to own the platform infrastructure that enables engineering teams to build and operate AI systems efficiently and reliably. This is a senior individual contributor role focused on model serving, vector infrastructure, data pipelines, observability, and deployment platforms. Your success will be measured by the productivity and reliability gains you enable for other engineers.</p> <p>You will combine deep platform engineering expertise with a strong understanding of AI workload requirements, including inference performance, retrieval systems, and operational complexity.</p> <p>At the staff level, you will:</p> <ul> <li>Define and evolve the technical foundations of the AI platform</li> <li>Build systems that accelerate engineering teams across the organisation</li> <li>Set standards for reliability, security, and scalability</li> <li>Lead architecture decisions and evaluate emerging technologies</li> <li>Use AI coding tools such as Codex, Claude Code, or similar as part of your daily workflow</li> </ul> <h4>Key responsibilities</h4> <p><strong>AI platform infrastructure</strong><br> Design and operate model serving infrastructure using vLLM, TGI, TensorRT-LLM, or similar technologies.<br> Build and manage vector infrastructure, embedding pipelines, and retrieval systems.<br> Develop data pipelines for document ingestion, transformation, and storage.<br> Own deployment platforms, CI/CD pipelines, and infrastructure-as-code.</p> <p><strong>Observability & reliability</strong><br> Build observability across the AI stack, including latency, throughput, and model behavior monitoring.<br> Define SLOs, perform capacity planning, and lead reliability engineering initiatives.<br> Lead incident response, root-cause analysis, and postmortems.<br> Establish platform standards for operational excellence.</p> <p><strong>Force multiplication</strong><br> Build reusable internal tooling, SDKs, and deployment patterns.<br> Partner with engineering teams to translate infrastructure needs into scalable platform capabilities.<br> Mentor engineers and raise technical standards across the organisation.</p> <h4>Basic qualifications</h4> <ul> <li>10+ years of platform, infrastructure, or backend engineering experience</li> <li>Proven experience operating at staff or principal engineer level</li> <li>Deep expertise in Azure (AWS or GCP also valued)</li> <li>Strong Kubernetes and Docker experience</li> <li>Hands-on experience with Terraform, CI/CD pipelines, and infrastructure-as-code</li> <li>Experience building production data pipelines</li> <li>Proficiency in Python, Java/Kotlin, or Go</li> <li>Strong PostgreSQL knowledge at production scale</li> <li>Experience with distributed tracing, metrics, logging, and alerting</li> <li>Practical use of AI coding assistants such as Codex or Claude Code</li> <li>Excellent written and verbal communication skills</li> </ul> <h4>Preferred qualifications</h4> <ul> <li>Experience building RAG systems and retrieval infrastructure</li> <li>Experience with LangGraph, LangChain, Semantic Kernel, or similar frameworks</li> <li>Hands-on experience with GPU-based inference infrastructure</li> <li>Experience with vector databases such as pgvector, Qdrant, Pinecone, or Weaviate</li> <li>Familiarity with LLM inference optimization and evaluation frameworks</li> <li>Experience with speech and conversational AI systems</li> <li>Experience building internal developer platforms</li> <li>Security and compliance experience in highly regulated environments</li> </ul> <h4>Technology stack</h4> <p><strong>Backend & platform:</strong> Python, Java, Kotlin, Go, REST, gRPC<br> <strong>AI infrastructure:</strong> vLLM, TGI, TensorRT-LLM, GPU Serving<br> <strong>AI & LLM:</strong> LangChain, LangGraph, Microsoft Agent Framework, vector databases<br> <strong>Data:</strong> PostgreSQL, Redis, DocumentDB, Azure Blob Storage<br> <strong>Infrastructure:</strong> Azure, Docker, Kubernetes, Terraform, CI/CD<br> <strong>Observability:</strong> Langfuse, Grafana, Prometheus, OpenTelemetry<br> <strong>Security:</strong> Entra ID, RBAC, Secrets Management, Zero Trust<br> <strong>AI development:</strong> Codex, Claude Code, OpenCode</p> <h4>What we’re looking for</h4> <p>You are an engineer who:</p> <ul> <li>Thinks in systems and enables others to move faster</li> <li>Designs infrastructure that is reliable, scalable, and easy to operate</li> <li>Grounds technical decisions in evidence and operational data</li> <li>Raises engineering standards through architecture and mentorship</li> <li>Communicates clearly and transparently</li> <li>Embraces AI-native development practices</li> </ul> <h4>Why apply?</h4> <p>This role offers the opportunity to shape the technical foundations of a world-class AI platform supporting transformative public services in Abu Dhabi. You will tackle complex infrastructure challenges at the intersection of AI, cloud computing, and platform engineering while delivering meaningful impact at a national scale.</p></p><p></p>

<p><h4>Description</h4> <p><strong>Key responsibilities</strong></p> <ul> <li>Design, build, and maintain scalable data pipelines (ETL/ELT)</li> <li>Develop and optimize data architecture, data lakes, and warehouses</li> <li>Ensure data quality, reliability, and integrity across systems</li> <li>Collaborate with product, engineering, and analytics teams to define data needs</li> <li>Build real-time and batch data processing systems</li> <li>Optimize database performance and query efficiency</li> <li>Implement data governance, security, and best practices</li> <li>Mentor junior data engineers and promote engineering excellence</li> </ul> <p><strong>Requirements</strong></p> <ul> <li>5+ years of experience in data engineering or related roles</li> <li>Strong proficiency in Python and SQL — not just writing queries, but designing reusable, tested pipeline code</li> <li>Hands-on AWS experience (required): Redshift, S3, Athena, ECS, EventBridge</li> <li>Experience building and maintaining data warehouses — Redshift experience is a strong plus</li> <li>Familiarity with workflow orchestration tools such as Airflow or equivalent, including ECS-based scheduling patterns</li> <li>Experience with multi-database environments: PostgreSQL/Aurora and MySQL/MariaDB</li> <li>Strong understanding of data modeling, dimensional design, and schema evolution</li> <li>Experience with streaming technologies such as Kafka is a plus</li> <li>Comfort working in a fast-moving product company where priorities shift and pipelines must be resilient</li> </ul> <p><strong>Nice to have</strong></p> <ul> <li>Experience supporting machine learning pipelines</li> <li>Knowledge of data governance and privacy best practices</li> <li>Experience in fast-paced startups or product companies</li> <li>Exposure to BI tools (e.g., Metabase, Tableau, Power BI)</li> </ul></p><p></p>

<p><h4>Description</h4> <p><strong>Key responsibilities</strong></p> <ul> <li>Design, build, and maintain scalable data pipelines (ETL/ELT)</li> <li>Develop and optimize data architecture, data lakes, and warehouses</li> <li>Ensure data quality, reliability, and integrity across systems</li> <li>Collaborate with product, engineering, and analytics teams to define data needs</li> <li>Build real-time and batch data processing systems</li> <li>Optimize database performance and query efficiency</li> <li>Implement data governance, security, and best practices</li> <li>Mentor junior data engineers and promote engineering excellence</li> </ul> <p><strong>Requirements</strong></p> <ul> <li>5+ years of experience in data engineering or related roles</li> <li>Strong proficiency in Python and SQL — not just writing queries, but designing reusable, tested pipeline code</li> <li>Hands-on AWS experience (required): Redshift, S3, Athena, ECS, EventBridge</li> <li>Experience building and maintaining data warehouses — Redshift experience is a strong plus</li> <li>Familiarity with workflow orchestration tools such as Airflow or equivalent, including ECS-based scheduling patterns</li> <li>Experience with multi-database environments: PostgreSQL/Aurora and MySQL/MariaDB</li> <li>Strong understanding of data modeling, dimensional design, and schema evolution</li> <li>Experience with streaming technologies such as Kafka is a plus</li> <li>Comfort working in a fast-moving product company where priorities shift and pipelines must be resilient</li> </ul> <p><strong>Nice to have</strong></p> <ul> <li>Experience supporting machine learning pipelines</li> <li>Knowledge of data governance and privacy best practices</li> <li>Experience in fast-paced startups or product companies</li> <li>Exposure to BI tools (e.g., Metabase, Tableau, Power BI)</li> </ul></p><p></p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Key Responsibilities Lead the design, development, testing, and deployment of software applications.<br> Provide technical leadership and mentorship to the development team.<br> Review code to ensure quality, maintainability, security, and adherence to coding standards.<br> Collaborate with Product Managers, Architects, QA, and stakeholders to define technical solutions.<br> Break down business requirements into technical tasks and guide implementation.<br> Drive Agile development practices, sprint planning, and technical estimations.<br> Troubleshoot complex technical issues and provide effective solutions.<br> Ensure application performance, scalability, reliability, and security.<br> Promote DevOps, CI/CD, automated testing, and software engineering best practices.<br> Stay updated with emerging technologies and recommend process and technology improvements.<br> Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.<br> 7+ years of software development experience, including at least 2 years in a technical leadership role.<br> Strong proficiency in one or more programming languages such as Java, C#, Python, JavaScript/TypeScript, or Go.<br> Experience with modern frameworks and cloud platforms (AWS, Azure, or Google Cloud).<br> Strong understanding of RESTful APIs, microservices architecture, databases (SQL/NoSQL), and system design.<br> Experience with Git, CI/CD pipelines, Docker, Kubernetes, and Agile/Scrum methodologies.<br> Excellent analytical, problem-solving, communication, and stakeholder management skills.<br> Preferred Qualifications Experience leading distributed or remote engineering teams.<br> Cloud certifications (AWS, Azure, or Google Cloud).<br> Knowledge of DevSecOps, Infrastructure as Code (Terraform), and container orchestration.<br> Experience with monitoring and observability tools.<br> Arabic speaker is mandatory</span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <p>We are seeking an experienced Engineering Manager for our AI Product &amp; Platform team, a pivotal role that combines technical acumen with leadership skills. This position offers a unique opportunity to lead innovative projects that will shape the future of AI technologies within our organization. As the Engineering Manager, you will be responsible for guiding a talented team of engineers, fostering a collaborative environment, and ensuring the successful delivery of AI solutions that drive business value.</p><p>In this dynamic role, you will have the chance to influence the strategic direction of our AI initiatives. You will work closely with cross-functional teams, including product management and data science, to align engineering efforts with business goals. Your leadership will not only enhance team performance but also contribute to the overall growth of our AI capabilities. We prioritize a culture of continuous learning and professional development, providing ample opportunities for you to expand your skills and advance your career.</p><p>Joining our team means becoming part of a forward-thinking organization that values innovation and creativity. We are committed to supporting our employees' career progression through mentorship programs and training workshops. As the Engineering Manager, you will play a crucial role in shaping the future of our AI platform, making a significant impact on our product offerings and customer satisfaction.</p><p><b>Responsibilities:</b></p><ol><li>Lead and manage a team of engineers, providing guidance and support to ensure high-quality deliverables while fostering a positive team culture.</li><li>Develop and implement engineering best practices, processes, and methodologies to enhance productivity and efficiency within the AI product development lifecycle.</li><li>Collaborate with product managers and stakeholders to define project scopes, timelines, and deliverables, ensuring alignment with strategic business objectives.</li><li>Oversee the architecture and design of AI systems, ensuring scalability, reliability, and performance while utilizing the latest technologies.</li><li>Drive continuous improvement initiatives by analyzing team performance metrics and implementing solutions to optimize workflows and enhance team collaboration.</li><li>Facilitate technical discussions and decision-making processes, ensuring that the team remains focused on delivering innovative solutions that meet customer needs.</li><li>Mentor and coach team members, providing opportunities for skill development and career advancement within the organization.</li><li>Stay current with industry trends and emerging technologies in AI, integrating relevant advancements into the team's projects and initiatives.</li><li>Manage project budgets and resources effectively, ensuring that engineering efforts are aligned with financial and operational goals.</li></ol> </div><h2 class="h5">Skills</h2> <div data-jb-field="skills"><ul><li>Proven experience in managing engineering teams, with a strong focus on AI technologies.</li><li>Expertise in software development methodologies, including Agile and DevOps practices.</li><li>Strong analytical and problem-solving skills to effectively address complex engineering challenges.</li><li>Excellent communication and interpersonal skills to facilitate collaboration across teams and stakeholders.</li><li>Ability to drive strategic initiatives and influence cross-functional teams towards achieving business goals.</li><li>Proficiency in programming languages commonly used in AI development, such as Python or Java.</li><li>Experience with cloud computing platforms and tools that support AI and machine learning applications.</li></ul></div>