Job Description

1. Lead the DMO AI Risk Governance & Validation Team

• Lead a four-member specialist team covering AI model governance, model validation, AI testing / QA / evaluations, and AI engineering for governance validation.
• Set the operating cadence, work allocation, review standards, escalation protocols and evidence-quality expectations for the team.
• Build a practical Line 1.5 AI governance capability that supports AI use case intake coordination, preliminary risk triage, evidence completeness checking, control effectiveness challenge and post-deployment governance.
• Ensure the team remains a technical assurance and evidence-review function, not an AI delivery owner, model owner or final risk approval owner.
• Develop team capabilities across AI governance, model risk, model validation, testing, data governance, GenAI, agentic AI, MLOps and regulatory expectations.

2. Operate Line 1.5 AI Governance Review and Challenge

• Review AI use cases from data governance, data risk, model lifecycle and practical control perspectives before escalation to independent risk and compliance stakeholders.
• Challenge the completeness and quality of AI review submissions, including business purpose, data sources, model / agent design, preliminary risk classification, validation evidence, control evidence and monitoring plans.
• Review whether AI controls are operationally workable, evidenced and sustainable, rather than policy-level only.
• Coordinate with Business / Use Case Owners, Data Owners, AI delivery teams, Technology / Security teams, Risk Management Department, Compliance Department, Legal Department, local data protection officers and other relevant control functions.
• Provide clear review conclusions, challenge points and remediation recommendations to support independent risk review and governance committee decision-making.

3. Data Governance for AI

• Review AI input data sources, data ownership, data classification, personal data / PII treatment, data access controls, cross-border considerations, data quality, lineage, metadata and evidence readiness.
• Confirm whether data used for AI use cases is appropriately approved, fit for purpose, traceable and subject to adequate controls.
• Challenge whether data access, storage location, output retention, SharePoint / platform access and downstream use are consistent with applicable data governance and privacy requirements.
• Work with Data Governance, Data Owners and control functions to establish a unified evidence layer covering data classification, data access, cross-border sharing, data protection, data quality, lineage, metadata and AI data risk assessment.
• Support AI-ready data governance standards, evidence templates and review checklists.

4. Model / Agent Validation Challenge

• Oversee review and challenge of model / agent validation evidence, including methodology, assumptions, feature logic, data inputs, limitations, performance, stability, robustness, explainability and monitoring design.
• Ensure validation evidence is appropriate for use case risk level, intended use and AI lifecycle stage.
• Challenge model / agent performance metrics, drift monitoring, bias / fairness assessment, robustness testing, output accuracy testing and human-in-the-loop controls.
• Work with model developers, AI engineers, data scientists and independent risk reviewers to ensure validation artefacts are clear, complete and decision-useful.
• Ensure validation evidence clearly identifies limitations, residual risks, control gaps and remediation actions where required.

5. AI Testing, QA and Evaluation Oversight

• Oversee testing approaches for statistical models, ML models, LLM applications and agentic AI systems.
• Ensure coverage of functional testing, regression testing, scenario-based testing, edge cases, adverse / irregular scenarios, bias / fairness evaluation, robustness analysis, drift detection and workflow reliability.
• Review end-to-end AI workflows, including data inputs, feature transformations, task completion, tool-use accuracy, API / connector behaviour, multi-step reasoning and output quality.
• Promote test logs, evaluation results, benchmarking, traceability and observability evidence to support AI risk review.
• Ensure testing findings are documented clearly and translated into remediation actions, risk caveats or acceptance considerations for governance forums.

6. AI Engineering and MLOps Governance

• Provide leadership oversight over technical review of AI engineering, agentic workflows, RAG, API integration, tool-use orchestration, Copilot Studio / Power Automate-type workflows, logging and monitoring controls.
• Challenge whether AI systems have appropriate MLOps / lifecycle controls, including model registry, version control, deployment controls, monitoring, retirement triggers and change management.
• Review whether AI / GenAI solutions disable inappropriate model training or data leakage pathways where required.
• Assess whether technical architecture and workflow design support auditability, explainability, resilience and responsible AI expectations.
• Partner with Technology, Security and AI delivery teams to embed technical controls early enough in the lifecycle.

7. Governance Framework, Procedures and Evidence Standards

• Translate AI governance policy, risk appetite and regulatory expectations into practical review procedures, templates, evidence packs and operating standards.
• Maintain AI review checklists and evidence standards covering lifecycle governance, data handling, access control, validation, testing, monitoring and control effectiveness.
• Ensure DMO review outputs are structured, audit-ready and reusable for independent risk review and committee escalation.
• Develop reporting on review pipeline, common evidence gaps, key control weaknesses, review turnaround, remediation status and recurring AI risk themes.
• Support continuous improvement of the AI Risk Governance operating model, including workflow tooling, intake process, inventory integration and review status tracking.

8. Senior Stakeholder Management and Regulatory Readiness

• Act as the senior DMO point of contact for AI risk governance matters with Risk Management Department, Compliance Department, Technology / Security teams, AI delivery teams, business use case owners and senior management.
• Explain complex AI, data and model-control issues in a clear, practical and decision-oriented manner for senior stakeholders.
• Support management discussions on AI governance, Line 1.5 operating model, staffing, capability build-out and APAC implementation roadmap.
• Contribute to regulator-ready documentation and evidence where AI governance, data handling, model validation or control effectiveness needs to be demonstrated.
• Bring external market awareness of AI governance practices in regulated financial services and adapt them to the APAC DMO operating model.