Head of AI Technology

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. 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. For more information, please visit www.zaincash.iq. Responsibilities: 1. 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. Work with business and technology stakeholders to evaluate AI ideas based on business value, feasibility, data readiness, cost, risk, and implementation complexity. Build and maintain an AI use case pipeline with clear prioritization, expected impact, ownership, and delivery roadmap. 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. Lead technical evaluation of AI platforms, models, tools, APIs, and vendors. Define the right architecture for each use case, balancing accuracy, cost, latency, security, scalability, and maintainability. Guide engineering teams on AI integration patterns, APIs, model deployment, observability, testing, and production readiness. 3. Proof of Concept and Production Delivery Lead AI proof of concepts from problem framing to testing and business validation. Define success metrics for each AI use case, including accuracy, automation rate, cost saving, fraud reduction, customer experience improvement, or operational efficiency. Ensure successful use cases are transitioned from PoC to production with proper governance, monitoring, documentation, and support model. Avoid AI for the sake of AI by ensuring every solution has a clear business case and measurable value. 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. Work with Information Security, Risk, Compliance, Legal, and Internal Audit to ensure AI solutions are aligned with regulatory and internal control requirements. Evaluate AI solutions for data leakage, hallucination risk, bias, misuse, operational risk, and vendor dependency. Define approval gates for AI use cases before they are deployed into production. 5. Data and Platform Readiness Assess the availability, quality, and accessibility of data required for AI use cases. Work with data, application, infrastructure, and security teams to improve AI readiness across ZainCash platforms. Support the creation of reusable AI capabilities, such as document processing, knowledge search, customer support assistants, fraud signals, workflow automation, and internal copilots. Promote reusable patterns instead of isolated experiments. 6. Vendor and Partner Evaluation Evaluate AI vendors, cloud AI services, local models, open source frameworks, and specialized fintech AI solutions. Run structured vendor assessments covering technical fit, security, data residency, cost, integration effort, support, and long term sustainability. Support procurement and management in making informed build versus buy decisions. 7. Team Enablement and Knowledge Sharing Mentor engineers, analysts, product owners, and business teams on practical AI usage. Create awareness sessions, internal guidelines, and reusable templates for AI opportunity assessment. Support the development of internal AI capabilities and reduce dependency on external vendors where possible. Bachelor degree in Computer Science, Software Engineering, Data Science, AI, or a related technical field. 8 plus years of overall technology experience, with at least 3 years in AI, machine learning, data science, or advanced analytics. Strong hands on understanding of modern AI concepts, including LLMs, RAG, embeddings, prompt engineering, AI agents, computer vision, document AI, predictive analytics, and MLOps. Strong software engineering background, preferably with Python and API based system integration. Experience designing and delivering production grade AI or data driven solutions. Good understanding of cloud AI services, managed ML platforms, open source AI frameworks, and model deployment approaches. Strong understanding of data privacy, security, responsible AI, and model governance. Ability to communicate clearly with both technical and non technical stakeholders. Strong problem solving skills and ability to challenge unclear or low value AI ideas. Preferred Qualifications: Experience in fintech, banking, payments, telecom, financial services, or regulated industries. Experience with fraud detection, KYC automation, AML support, customer service automation, or transaction analytics. Experience with Arabic language AI use cases, OCR, document processing, or image based verification. Experience with OpenShift, Kubernetes, microservices, API gateways, CI/CD, and enterprise integration. Experience evaluating AI vendors and preparing business cases for technology investment. Knowledge of data platforms, data pipelines, BI, and analytics environments.