ZainCash jobs

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>About Zaincash ZainCash Iraq is a leading mobile wallet in Iraq and recognized as Forbes top Fintech company of 2023 and 2024 as well as GSMA’s Best Mobile Innovation Supporting Humanitarian Situations.<br> The company offers a range of consumer and business services including local and international money transfer, bill payments, companion payment cards, payroll, aid disbursement, and more.<br> For more information, please visit www.<br>zaincash.iq. Responsibilities: 1.<br> AI Strategy and Use Case Development Identify high value AI opportunities across customer experience, fraud detection, KYC, operations automation, risk management, compliance, customer support, marketing, analytics, and internal productivity.<br> Work with business and technology stakeholders to evaluate AI ideas based on business value, feasibility, data readiness, cost, risk, and implementation complexity.<br> Build and maintain an AI use case pipeline with clear prioritization, expected impact, ownership, and delivery roadmap.<br> 2. Solution Design and Technical Leadership Translate business problems into practical AI solution designs, including LLM based solutions, RAG, workflow automation, predictive models, document intelligence, image analysis, and intelligent agents.<br> Lead technical evaluation of AI platforms, models, tools, APIs, and vendors.<br> Define the right architecture for each use case, balancing accuracy, cost, latency, security, scalability, and maintainability.<br> Guide engineering teams on AI integration patterns, APIs, model deployment, observability, testing, and production readiness.<br> 3. Proof of Concept and Production Delivery Lead AI proof of concepts from problem framing to testing and business validation.<br> Define success metrics for each AI use case, including accuracy, automation rate, cost saving, fraud reduction, customer experience improvement, or operational efficiency.<br> Ensure successful use cases are transitioned from PoC to production with proper governance, monitoring, documentation, and support model.<br> Avoid AI for the sake of AI by ensuring every solution has a clear business case and measurable value.<br> 4. AI Governance, Risk, and Compliance Establish practical AI governance standards covering data privacy, security, responsible AI, model risk, explainability, auditability, and human in the loop controls.<br> Work with Information Security, Risk, Compliance, Legal, and Internal Audit to ensure AI solutions are aligned with regulatory and internal control requirements.<br> Evaluate AI solutions for data leakage, hallucination risk, bias, misuse, operational risk, and vendor dependency.<br> Define approval gates for AI use cases before they are deployed into production.<br> 5. Data and Platform Readiness Assess the availability, quality, and accessibility of data required for AI use cases.<br> Work with data, application, infrastructure, and security teams to improve AI readiness across ZainCash platforms.<br> Support the creation of reusable AI capabilities, such as document processing, knowledge search, customer support assistants, fraud signals, workflow automation, and internal copilots.<br> Promote reusable patterns instead of isolated experiments.<br> 6. Vendor and Partner Evaluation Evaluate AI vendors, cloud AI services, local models, open source frameworks, and specialized fintech AI solutions.<br> Run structured vendor assessments covering technical fit, security, data residency, cost, integration effort, support, and long term sustainability.<br> Support procurement and management in making informed build versus buy decisions.<br> 7. Team Enablement and Knowledge Sharing Mentor engineers, analysts, product owners, and business teams on practical AI usage.<br> Create awareness sessions, internal guidelines, and reusable templates for AI opportunity assessment.<br> Support the development of internal AI capabilities and reduce dependency on external vendors where possible.<br> Bachelor degree in Computer Science, Software Engineering, Data Science, AI, or a related technical field.<br> 8 plus years of overall technology experience, with at least 3 years in AI, machine learning, data science, or advanced analytics.<br> Strong hands on understanding of modern AI concepts, including LLMs, RAG, embeddings, prompt engineering, AI agents, computer vision, document AI, predictive analytics, and MLOps.<br> Strong software engineering background, preferably with Python and API based system integration.<br> Experience designing and delivering production grade AI or data driven solutions.<br> Good understanding of cloud AI services, managed ML platforms, open source AI frameworks, and model deployment approaches.<br> Strong understanding of data privacy, security, responsible AI, and model governance.<br> Ability to communicate clearly with both technical and non technical stakeholders.<br> Strong problem solving skills and ability to challenge unclear or low value AI ideas.<br> Preferred Qualifications: Experience in fintech, banking, payments, telecom, financial services, or regulated industries.<br> Experience with fraud detection, KYC automation, AML support, customer service automation, or transaction analytics.<br> Experience with Arabic language AI use cases, OCR, document processing, or image based verification.<br> Experience with OpenShift, Kubernetes, microservices, API gateways, CI/CD, and enterprise integration.<br> Experience evaluating AI vendors and preparing business cases for technology investment.<br> Knowledge of data platforms, data pipelines, BI, and analytics environments.<br></span> </div>