AI & Data Architect

Specifically, the successful jobholder will be required to: Define and maintain the Bank’s AI and data architecture frameworks, standards, principles and strategic roadmaps, ensuring alignment with enterprise architecture. Establish data architecture standards for data modelling, integration, quality, metadata, lineage and master data management to support trusted, governed data products for AI and analytics. Translate business priorities into end-to-end AI and data solution architectures, covering data, models, applications, integration, security, monitoring and support. Develop reusable architecture patterns for machine learning, generative AI and agentic AI, incorporating appropriate governance, safeguards, monitoring and human oversight. Evaluate AI technologies, platforms, models and vendors, recommending fit-for-purpose solutions based on business value, cost, security, scalability, resilience and interoperability. Define standards for AI model deployment, monitoring and lifecycle management, including MLOps and LLMOps, in collaboration with technology, data, security and infrastructure teams. Lead architecture reviews, document design decisions and risks, and ensure compliance with the Bank’s architecture governance frameworks and technical standards. Collaborate with cross-functional teams to resolve design dependencies and ensure solutions meet business, operational, security, privacy, performance and regulatory requirements. Maintain AI and data architecture documentation, capability maps, technology standards and transition roadmaps, while providing technical leadership to delivery teams. Guide AI infrastructure capacity, performance and cost optimisation, ensuring scalable, efficient and sustainable AI adoption across the Bank.

Skills, Competencies and Experience

The successful candidate will be required to have the following skills and competencies:

A bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science or a related field.

A relevant master’s degree is an added advantage.

Professional certification in enterprise architecture, cloud architecture, data architecture or AI architecture is an added advantage.

At least eight (8) years of experience in data, software or solution architecture, including at least three (3) years designing AI, machine learning or advanced analytics solutions.

Demonstrated experience designing and delivering enterprise-scale data platforms and production machine learning solutions, including integration of analytical or AI services into operational applications. Relevant data-platform experience may include data lakes, lakehouses or data warehouses.

Practical experience designing cloud and hybrid architectures, including containerised deployment and API-led and event-driven integration, with the ability to assess technology choices and implementation trade-offs.

Experience in financial services, banking, fintech, telecommunications or another highly regulated industry.