Requirements
Bachelor’s degree in Computer Science, Software Engineering, Data Science, Information Technology, or a related discipline. – Master’s degree in Artificial Intelligence, Data Science, Computer Science, or an MBA with a technology focus is strongly preferred.
▶ Professional Certifications – Professional certification in a major AI or cloud platform (Microsoft Azure AI Engineer / Solutions Architect, AWS Machine Learning or Solutions Architect, Google Cloud Professional ML Engineer, or equivalent). – Enterprise or solution architecture certification such as TOGAF is advantageous. – Project or programme management certification (PMP, PRINCE2, or Agile/SAFe) is advantageous. – Credentials in AI governance, risk, or data privacy (for example ISO/IEC 42001 or CIPP) are an asset.
▶ Experience – 8–10 years of experience in data, automation, AI, Generative AI, analytics, or digital transformation initiatives. – Demonstrated ownership of an AI or automation roadmap at group, enterprise, or multi-entity level. – Proven experience designing and deploying Generative AI or Agentic AI solutions into production, not solely proofs of concept. – Track record of leading vendor evaluation, solution selection, and third-party implementation governance. – Experience working within or alongside enterprise application, infrastructure, and cybersecurity functions in a complex, multi-entity organisation. – Experience leading technical teams and influencing senior business stakeholders; regional or UAE experience is an advantage.
▶ Key Skills & Attributes – Strong command of Agentic AI frameworks and enterprise GenAI platforms, with the depth to challenge vendor claims credibly. – Ability to translate ambiguous business problems into prioritised, technically feasible AI solutions. – Excellent written and verbal communication, including executive-level presentation and business case writing. – Sound commercial judgement on build-versus-buy, licensing, and consumption cost. – Rigorous approach to security, data governance, and auditability of automated decisions. – Collaborative leadership style with the resilience to drive change across independently managed operating companies.
Description
▶ Strategy, Roadmap & Portfolio Ownership – Own and continuously evolve the Group AI and Agentic AI roadmap, aligning it to the GCIO’s technology strategy and to the commercial priorities of each operating company. – Work with the GCIO, business leaders, and functional heads to identify, qualify, and document candidate business use cases across finance, procurement, supply chain, sales, service, and human capital. – Prioritise AI initiatives using a structured assessment of business value, technical feasibility, risk exposure, and data readiness, and maintain a transparent portfolio view for executive review. – Develop business cases, cost models, and benefit-tracking mechanisms so that AI investment decisions are evidence-based and outcomes are measurable post-deployment. – Maintain an informed position on the AI and Agentic AI market, advising leadership on emerging capabilities, obsolescence risk, and the Group’s competitive positioning. ▶ Agentic AI Solution Design & Architecture – Design Agentic AI solutions and workflows, defining agent roles, task decomposition, orchestration patterns, memory and context strategies, and success criteria. – Define how agents interact with enterprise systems and knowledge sources — ERP, CRM, HRMS, procurement platforms, the data lake, document repositories, email, and internal and external APIs. – Build solution blueprints covering multi-agent workflows, human-in-the-loop approval points, escalation rules, fallback behaviour, and complete audit trails for every automated action. – Ensure solution architecture is scalable, secure, reusable, and fully aligned with enterprise IT standards, reference architectures, and integration patterns. – Establish reusable components, prompt libraries, evaluation harnesses, and design patterns that reduce the cost and lead time of each subsequent AI initiative. ▶ Vendor Evaluation & Delivery Governance – Lead vendor evaluation and solution selection, defining requirements, running structured proofs of concept, and comparing platforms on capability, cost, security posture, and roadmap credibility. – Negotiate scope and deliverables with implementation partners in coordination with Procurement and Legal, and hold partners accountable to agreed quality and timeline commitments. – Provide implementation governance across the AI portfolio — stage gates, architecture review, testing standards, deployment readiness, and post-go-live benefit review. – Manage AI programme budgets, consumption costs, and licensing, ensuring predictable and optimised spend as workloads move from pilot to production.▶ Governance, Risk & Responsible AI – Define and enforce the Group’s Responsible AI framework, covering permitted use cases, data classification, model selection, human oversight, and prohibited applications. – Work with Cybersecurity, Legal, Compliance, and Internal Audit to ensure AI solutions meet UAE data protection requirements and internal control expectations. – Establish monitoring for model drift, hallucination risk, bias, and unintended agent behaviour, with clear thresholds for intervention or rollback. – Maintain documentation and audit evidence sufficient to explain any automated decision to internal audit, external auditors, or a regulator. ▶ Team Leadership & Business Enablement – Lead and develop a multidisciplinary team of AI engineers, agent developers, and data specialists, setting technical direction and quality standards. – Work closely with enterprise application, emerging technology, infrastructure, and cybersecurity teams to ensure AI capability is embedded rather than isolated. – Build AI literacy across the Group through structured enablement, communities of practice, and clear guidance on safe and effective everyday use. – Act as the Group’s internal authority on AI, translating technical capability into plain commercial language for boards, executives, and operating company management.