Head of AI and Automation

Key Responsibilities

Strategic Leadership & Delivery: Lead the AI and Automations pillar—encompassing Data Science, MLOps, and Intelligent Automation—while executing use cases aligned with Implementation Matrix priorities and managing associated research/development budgets.

AI Governance & Compliance: Establish robust AI governance frameworks, model risk management practices, and ethical AI principles. Ensure all solutions meet strict regulatory, ethical, and technical standards, incorporating international frameworks like ISO 42001, NIST, or the EU AI Act.

Innovation & Lifecycle Management: Lead the development of AI proofs-of-concept (POCs) and oversee their transition to secure production environments, managing the full AI lifecycle (monitoring, retraining, and optimization).

Ecosystem & Partnerships: Build strategic partnerships with universities, research institutions, and technology vendors, representing the Centralized Data Office in external engagements and fostering a culture of responsible experimentation.

Qualifications

Bachelor’s and Master’s Degrees in a quantitative or technical field (e.g., Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, or Information Systems).

PhD in Artificial Intelligence, Machine Learning, Data Science, Intelligent Automation, or a related discipline is an added advantage.

Experience & Technical Competencies

General Experience: Minimum of 10 years in AI, Machine Learning, Data Science, and Intelligent Automation, including at least 5 years leading enterprise solution delivery and 3 years managing multi-disciplinary technical teams within highly regulated environments (financial services, central banking, telecom, or government).

Technical Expertise: Deep knowledge of classic AI, Deep Learning, NLP, Large Language Models (LLMs), Generative AI, AI Agents, RAG, and intelligent automation platforms (RPA, Power Automate, UiPath).

Architecture & Cloud: Hands-on experience designing enterprise AI architectures, integrating APIs and cloud-based AI platforms (Azure AI/OpenAI, Databricks, AWS, Google AI), and implementing controls for model explainability, security, and PII compliance.