AI Technical Risk Lead, Data & AI Risk

About the role

DBS is hiring an ED/SVP, AI Technical Risk Lead within its Risk Management Group's Data & AI Risk function. This second-line function provides independent risk oversight of data and AI risks, owns the Group Data and AI Risk Management Policies and their control frameworks, and reports risk to senior management, Board committees and regulators. The role provides deep technical AI risk expertise and translates risk requirements into practical technical control expectations with Technology, Security and platform teams.

Key responsibilities

• Act as the subject matter expert for technical AI risks from AI/ML platforms, foundation models, agents, RAG architectures and emerging AI technologies, advising on new capabilities and deployment patterns.
• Define technical control expectations for AI platforms, models, agents and infrastructure, covering access management, data protection, model behaviour, monitoring, resilience and security guardrails.
• Partner with technology, architecture and engineering teams, provide independent second-line challenge and help resolve technical risk issues.
• Evaluate emerging AI technologies and deployment patterns, and recommend enhancements to risk management approaches.
• Support regulatory engagement, industry consultations and supervisory reviews, and contribute to the evolution of the AI Risk Framework.

Required profile

• Significant experience in AI engineering, technology architecture, AI security, machine learning, AI governance, technology risk or related fields.
• Degree in Computer Science, Engineering, Data Science, Information Security or a related discipline; advanced qualifications are advantageous.
• Experience in financial services is beneficial but not required; experience in technology firms, cloud providers or AI platform providers is highly regarded.
• Ability to engage credibly with senior engineers, architects and technology leaders.

Required skills

• Strong understanding of AI/ML architectures, foundation models, GenAI and agentic systems, including RAG and model lifecycle management.
• Understanding of AI-specific risks such as model misuse, hallucinations, prompt injection, data leakage and third-party model dependencies.
• Familiarity with technical control concepts, technology risk and cyber risk management principles, and awareness of global AI regulatory developments.