About UOB

United Overseas Bank Limited (UOB) is a leading bank in Asia with a global network of more than 500 branches and offices in 19 countries and territories in Asia Pacific, Europe and North America. In Asia, we operate through our head office in Singapore and banking subsidiaries in China, Indonesia, Malaysia and Thailand, as well as branches and offices. Our history spans more than 80 years. Over this time, we have been guided by our values – Honorable, Enterprising, United and Committed. This means we always strive to do what is right, build for the future, work as one team and pursue long-term success. It is how we work, consistently, be it towards the company, our colleagues or our customers.

Job Description

UOB's AI & Data Risk Governance & Control is a centralized Line 2 function within Group Risk Management, responsible for providing independent oversight and effective challenge across the Bank's AI ecosystem. The function ensures that AI systems, including Generative AI and Agentic AI, are secure, fair, explainable, resilient, and appropriately governed throughout their lifecycle. Leveraging both risk expertise and hands-on technical capabilities, the team conducts independent assessments, validation activities, control evaluations, and horizon scanning to identify emerging risks, assess evolving technologies, and provide objective, decision-oriented guidance to senior management. This role does not act as a solution owner or production development team, rather, it applies technical expertise to independently evaluate, test, validate, and challenge AI solutions developed by business units, technology teams, vendors, or third parties.

Role Overview

This is a hands-on individual contributor role focused on Generative AI and Agentic AI risk governance, assessment, and oversight. The role is responsible for executing end-to-end AI use case risk assessments, monitoring frontier AI technologies and emerging risks, conducting applied research, and communicating decision-oriented insights to senior management. The role also requires sufficient technical expertise to develop prototypes, evaluation environments, and proof-of-concepts to independently assess, challenge, evaluate, and test AI systems proposed or developed by AI builders. The role does not function as a solution owner or production developer; technical capabilities are applied to support effective challenge, validation, control testing, and risk assessment activities.

Key Responsibilities

• Perform independent review and effective challenge of AI use cases, covering Generative AI and Agentic AI. Execute business-as-usual risk assessment activities, including inherent risk assessment, control effectiveness assessment, residual risk assessment, remediation tracking, and governance reporting. Apply third-party risk assessment processes where external providers or solutions are involved.
• Scan the market and research the frontier for emerging AI technologies, attack techniques, incidents, regulatory developments, standards, and industry practices. Assess the implications for the bank and present clear, decision-oriented findings to senior management.
• Develop proof of concepts, prototypes, evaluation environments and reference implementations for Generative AI and Agentic AI solutions to support independent validation, risk assessment, control testing, and effective challenge. Understand key architectural components including models, orchestration frameworks, agents, tools, retrieval systems, APIs, data pipelines and deployment patterns.
• Conduct applied research specific to Generative AI and Agentic AI to strengthen validation methodologies, evaluation approaches, metrics, test suites, and internal guidance.
• Apply domain knowledge of AI risk evaluations, covering relevant threats and failure modes such as prompt injection, jailbreak, data poisoning, distribution shift, unsafe tool use, and agentic failure modes.
• Apply working knowledge of relevant AI risk frameworks and industry guidance, including GenAI/Agentic AI papers from MAS, IMDA, ABS, OWASP, SAFR and so on.

Education Requirements

• University graduate in Computer Science, Data Science, Statistics, Applied Mathematics, Electrical or Computer Engineering, or a related quantitative field. A postgraduate or research background in machine learning, Generative AI, Agentic AI, or AI safety is an advantage.
• Certifications in responsible AI, model risk management, AI security, privacy, or related risk and governance domains are preferred.

Job Requirements

• Experience: 5–10 years of relevant experience in AI or AI and data governance, preferably in banking or financial services.
• Independent validation and business-as-usual risk assessment: Design and execute independent validation activities including evaluation design, benchmark construction, test-suite development, scenario testing, uncertainty analysis and monitoring effectiveness reviews.
• Third-party risk assessment: Knowledge of risk assessment processes applicable to externally provided AI vendors, models, platforms, services, or data.
• AI risk controls and guardrails: Knowledge of relevant AI control and guardrail patterns, operating-effectiveness testing, and supporting evidence for Generative AI and Agentic AI.
• Experience conducting adversarial testing, red teaming, evaluation benchmarking, model behavior analysis and AI control effectiveness testing across Generative AI and Agentic AI systems.
• Market research and senior-management communication: Ability to conduct structured horizon scanning and communicate concise, decision-oriented findings and recommendations to senior management.
• Hands-on experience developing prototypes or proof-of-concepts using modern AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, OpenAI, Anthropic, Azure AI, AWS Bedrock or equivalent platforms. Ability to understand and assess architectures involving RAG, agents, tool use, MCP, orchestration frameworks, vector databases, model gateways and AI observability tooling.
• Applied research: Ability to critically assess research, standards, and industry practices; design or review experiments; and translate findings into validation methods, evaluation metrics, test suites, and internal guidance for Generative AI and Agentic AI.
• Communication and effective challenge: Ability to communicate technical and risk findings clearly to technical and non-technical stakeholders.
• Delivery model: Self-directed individual contributor capable of working across multidisciplinary teams.

Additional Requirements

Be a Part of the UOB Family

UOB is an equal opportunity employer. UOB does not discriminate on the basis of a candidate's age, race, gender, color, religion, sexual orientation, physical or mental disability, or other non-merit factors. All employment decisions at UOB are based on business needs, job requirements and qualifications. If you require any assistance or accommodations to be made for the recruitment process, please inform us when you submit your online application.

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