ED/SVP, AI Technical Risk Lead, Data & AI Risk, Risk Management Group

About Risk Management Group and Data & AI Risk

Risk Management Group (RMG) is DBS's second line of defence, providing independent risk oversight and challenge across the Group. Within RMG, Data and AI Risk (DAR) is the dedicated second-line function for data and AI risk. The data risk capability is an established Line 2 vertical; the AI risk capability was stood up in 2026 and covers traditional AI/ML, GenAI and agentic AI.

DAR owns the Group Data Risk Management Policy and the Group AI Risk Management Policy, together with their supporting risk taxonomies and control frameworks. It provides independent review and challenge of data and AI risks, monitors key risks and escalates gaps, delivers independent BCBS 239 Compliance Validation, and reports to senior management, Board-level committees and regulators. The function is organised around four capabilities: Data Risk (framework & policy, oversight & monitoring, compliance validation), AI Risk (framework & policy, and AI risk integration), Risk Analytics & Enablement, and a Regional DAR Lead covering International Centres.

Job purpose

Provide deep technical AI risk expertise for the Bank's AI ecosystem. Translate AI risk requirements and control objectives into practical technical control expectations, partnering with Technology, Security and platform teams to ensure AI solutions are designed and operated within the Bank's risk appetite.

Act as the primary technical risk subject matter expert within Data & AI Risk for emerging AI capabilities including GenAI, AI platforms and agentic AI, helping shape technical control approaches, governance expectations and risk management practices as AI adoption scales across the Group.

Key accountabilities:

Technical AI Risk Leadership

• Act as DAR's subject matter expert for technical AI risks arising from AI/ML platforms, foundation models, agents, RAG architectures and emerging AI technologies.

• Provide technical risk guidance on the adoption of new AI capabilities, architectures and deployment patterns.

• Assess emerging industry developments and advise on implications for the Bank's AI risk management approach.

Technical Control Expectations

• Define technical control expectations for AI platforms, models, agents and supporting infrastructure.

• Translate AI risk policies, standards and control objectives into practical implementation expectations for technology and delivery teams.

• Develop guidance covering areas such as access management, data protection, model behaviour, agent permissions, monitoring, resilience and security guardrails.

• Define risk indicators, telemetry requirements and evidence expectations to support effective management of AI risks.

Technology Engagement and Challenge

• Partner with technology, architecture and engineering teams to identify and address technical AI risks.

• Provide independent second-line challenge where proposed technical solutions may not adequately address identified risks.

• Support the design of scalable technical controls and risk-management approaches for AI platforms and services.

• Facilitate resolution of technical risk issues, trade-offs and implementation challenges.

Emerging AI Risk Management

• Assess new and emerging AI technologies for potential risk implications.

• Evaluate evolving AI deployment patterns and recommend enhancements to risk management approaches where required.

• Support the development of risk guidance for emerging capabilities including autonomous agents, agent-to-agent interactions, foundation models and advanced reasoning systems.

• Identify areas where existing risk frameworks may require enhancement to address new sources of AI risk.

Regulatory Engagement and Industry Alignment

• Support interpretation of emerging regulatory expectations relating to AI risk management.

• Contribute technical expertise to regulatory engagements, industry consultations and supervisory reviews.

• Assess emerging industry standards and good practices and advise on implications for the Group.

• Support the continual evolution of the AI Risk Framework in response to technology and regulatory developments.

Key stakeholders and working relationships

• Head of Data and AI Risk and the DAR leadership team (primary).

• Risk and control owners across RMG risk families — technology, cyber, third-party, operational and others.

• Technology & Delivery — enterprise AI platform, control plane and observability teams.

• Legal & Compliance and Group Data Management on AI governance and data matters.

• Group Data and AI Committee and other senior governance forums.

Measures of success — first 12 to 18 months

• Technical AI risk requirements are clearly articulated and consistently understood across technology and engineering stakeholders.

• AI risk considerations are appropriately reflected in the design of AI platforms, services and governance processes.

• Material technical AI risks are identified and addressed early in solution design and implementation.

• Effective working relationships established across risk, technology and engineering functions.

• Technical AI risk guidance is recognised as credible, practical and actionable by stakeholders.

• The Bank's AI risk framework remains aligned to emerging AI technologies and evolving regulatory expectations.

Requirements:

Experience and qualifications

• Significant experience in at least one of AI engineering, technology architecture, AI security, machine learning, AI governance, technology risk or related disciplines.

• Practical experience working with AI/ML systems and understanding how associated risks can be identified, managed and controlled.

• Experience working with complex technology platforms and engaging with engineers, architects and technology leaders to translate technical concepts into practical risk management outcomes.

• Demonstrated ability to engage credibly with senior engineers, architects and technology leaders on AI architecture, platforms and associated risk considerations.

• Experience within financial services is beneficial but not required.

• Experience within technology firms, cloud providers, AI platform providers or organisations with mature AI deployment practices is highly regarded.

• Degree qualified in Computer Science, Engineering, Data Science, Information Security or a related discipline. Advanced qualifications are advantageous.

Technical knowledge and skills

• Strong understanding of AI/ML architectures, foundation models, GenAI and agentic systems.

• Familiarity with AI development and deployment patterns including RAG, orchestration frameworks, model lifecycle management and AI platforms.

• Understanding of AI-specific risks including model misuse, hallucinations, prompt injection, excessive autonomy, data leakage and third-party model dependencies.

• Understanding of technical control concepts including identity and access management, monitoring, observability, security controls, resilience and traceability.

• Familiarity with technology risk and cyber risk management principles.

• Awareness of global regulatory developments relating to AI and technology risk.

Competencies and attributes

• Deep technical curiosity and credibility.

• Strong analytical and problem-solving capability.

• Ability to translate complex technical concepts into clear risk implications.

• Independent thinker who provides constructive challenge while maintaining strong stakeholder relationships.

• Comfortable operating in rapidly evolving and ambiguous environments.

• Strong communication and influencing skills across both technical and non-technical audiences.

• Pragmatic and outcome-focused, balancing innovation and risk management objectives.

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