Assistant VP

Temasek is a global investment company headquartered in Singapore, with a net portfolio value of S$518 billion (US$401b, €350b, £304b, RMB2.77t) as at 31 March 2026. Our Purpose So Every Generation Prospers guides us to make a difference for today's and future generations. We seek to build a resilient and forward-looking portfolio that will deliver good sustainable returns over the long term.

We have 13 offices in 9 countries around the world: Beijing, Hanoi, Mumbai, Shanghai, Shenzhen, and Singapore in Asia; and Brussels, London, Mexico City, New York, Paris, San Francisco, and Washington, DC outside Asia.

For more information on Temasek, please visit
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Introduction

The AI Product Manager at Temasek owns the end-to-end product lifecycle for one or more internal AI products - from problem framing and business cases through to production deployment, adoption, and continuous improvement. You will work at the intersection of investment workflows, agentic AI platform capabilities, and user experience, defining what to build, why it matters, and how success is measured. You will not just work on custom-built products but also utilize an agentic platform to help build agentic automation for business. You will not throw requirements over a wall to engineering; you will be an active, technically engaged product leader embedded in cross-functional delivery teams.

Responsibilities

AI-native product ownership

• Own the full product lifecycle for assigned AI products: problem discovery, opportunity sizing, solution design, build prioritisation, launch, adoption tracking, and iterative improvement - with clear accountability for measurable business outcomes.
• Define product vision and strategy for AI use cases targeting Temasek's investment, portfolio monitoring, research, and operations workflows - translating senior stakeholder intent into concrete product roadmaps with sequenced delivery milestones.
• Make informed product decisions on AI system design: when to use RAG vs. fine-tuning vs. prompt engineering; how to structure agent workflows for reliability; when to inject human-in-the-loop checkpoints; and how to design evaluation frameworks that measure what actually matters to users.
• Drive rigorous product discovery: conduct user research with investment professionals, shadow real workflows, analyse process pain points, and synthesise findings into prioritised product backlogs with clear rationale.
  Own the product's evaluation strategy - defining success metrics, designing evaluation datasets, and maintaining ongoing benchmarks that track whether the AI product is improving or degrading over model and prompt versions.

• Translate complex, often implicit business needs from investment and operations teams into AI product specifications that are technically actionable - including data requirements, model capability requirements, integration touchpoints, and acceptable error/uncertainty tolerances.
• Develop and maintain a product requirements framework adapted for AI systems: covering system prompts and agent behaviour specifications, tool definitions and API contracts, evaluation rubrics, guardrail requirements, and user feedback collection mechanisms.
• Serve as the primary interface between business stakeholders and the engineering team - managing expectations about what AI systems can reliably do, communicating probabilistic outputs clearly to non-technical users, and building trust in AI products through transparent performance reporting.
• Build and maintain a business case framework for AI products - connecting product metrics (usage, accuracy, latency) to business value metrics (time saved, decision quality, risk reduced) in a format that supports ongoing investment justification.

• Lead cross-functional delivery squads comprising AI engineers, data engineers, UX designers, and domain specialists - maintaining delivery momentum, managing inter-team dependencies, and escalating blockers with clear context and proposed solutions.
• Partner with the AI Security & Governance Lead to ensure products meet governance requirements at every stage - embedding compliance into the product development process rather than treating it as a launch gate.
• Collaborate with the AI Fluency and Change Management team to design adoption programmes for launched AI products - defining the user education, rollout strategy, and feedback loops that convert deployment into sustained usage.
• Engage regularly with the platform engineering team to feed product-level requirements into platform capability planning - ensuring that common pat