AI & Data Architect

• Define and maintain the Bank's AI and data architecture frameworks, standards, principles and strategic roadmaps aligned to enterprise architecture
• Establish data architecture standards for data modelling, integration, quality, metadata, lineage and master data management
• 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 with governance, safeguards, monitoring and human oversight
• Evaluate AI technologies, platforms, models and vendors and recommend fit-for-purpose solutions
• Define standards for AI model deployment, monitoring and lifecycle management including MLOps and LLMOps
• Lead architecture reviews, document design decisions and risks and ensure compliance with architecture governance frameworks
• Maintain architecture documentation, capability maps, technology standards and transition roadmaps and provide technical leadership to delivery teams
• Guide AI infrastructure capacity, performance and cost optimisation

Qualifications & Experience:

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science or a related field
• 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
• Practical experience designing cloud and hybrid architectures, including containerised deployment and API-led and event-driven integration
• Experience in financial services, banking, fintech, telecommunications or another highly regulated industry
• A relevant master's degree and professional certification in enterprise, cloud, data or AI architecture are added advantages

Skills & Competencies:

• Enterprise architecture and design leadership
• Strong technology evaluation and trade-off analysis
• Data governance and standards definition
• Excellent stakeholder communication and documentation