Head of AI Technology

About the role

ZainCash Iraq, a leading mobile‑wallet provider recognized by Forbes and GSMA, is looking for a Head of AI Technology. The role will shape the company’s AI strategy, turn high‑value use cases into production‑ready solutions, and ensure responsible AI governance across the organization.

Key responsibilities

• Identify and prioritize high‑impact AI opportunities across customer experience, fraud detection, KYC, operations automation, risk management, compliance, support, marketing and analytics.
• Work with business and technology stakeholders to evaluate AI ideas based on value, feasibility, data readiness, cost, risk and complexity.
• Design practical AI solutions—including LLM‑based models, Retrieval‑Augmented Generation, workflow automation, predictive models, document intelligence, image analysis and intelligent agents—and define the appropriate architecture.
• Lead technical evaluation of AI platforms, models, tools, APIs and vendors, balancing accuracy, cost, latency, security, scalability and maintainability.
• Guide engineering teams on integration patterns, model deployment, observability, testing and production readiness.
• Run proof‑of‑concept projects from problem framing to business validation, set success metrics and transition validated use cases to production with proper governance.
• Establish AI governance standards covering data privacy, security, responsible AI, model risk, explainability, auditability and human‑in‑the‑loop controls.
• Assess data and platform readiness, collaborate with data, application, infrastructure and security teams to improve AI readiness across ZainCash platforms.

Required profile

• Proven ability to work with cross‑functional business and technology stakeholders to define and deliver AI solutions.
• Experience leading AI proof‑of‑concepts and guiding engineering teams through deployment and production.
• Strong understanding of AI governance, risk and compliance requirements.

Required skills

• Large Language Models (LLM) and Retrieval‑Augmented Generation (RAG)
• Predictive modeling and workflow automation
• Document intelligence and image analysis
• Intelligent agents and AI platform evaluation
• API integration, model deployment and observability
• AI governance, data privacy and security principles