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.
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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.
Business-to-AI translation
- 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.
Cross-functional execution
- 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 patterns across products are generalised into reusable platform components rather than rebuilt product-by-product.
Enterprise citizen developer enablement
- Design and deliver structured enablement programmes that equip non-technical employees to build low-code, no-code, and AI-assisted solutions using Temasek's approved enterprise platforms (e.g. Microsoft Power Platform, Copilot Studio, or equivalent AI tooling).
- Develop role-specific learning pathways for key employee segments — investment professionals, portfolio monitoring teams, operations, and support functions — that contextualize AI capability building within real workflow scenarios.
- Build and maintain a library of pre-validated citizen developer starter templates, prompt patterns, and agent workflow blueprints that teams can adapt without deep technical expertise.
- Run hands-on workshops, office hours, and embedded team sessions to accelerate capability from awareness to active production use.
Requirements
Experience and background
- 4–8 years of product management experience, with at least 2 years owning AI or ML products from problem framing through production deployment — ideally in a fast-moving AI-native organisation (OpenAI, Anthropic, Google DeepMind, or equivalent) or a financial institution with a serious AI product function.
- Demonstrated ability to ship AI products that real users adopt and depend on — not just prototypes or proofs of concept, but production systems with measurable impact and ongoing improvement cycles.
- Sufficient technical depth to engage substantively with AI engineers: able to review and critique prompt architecture, evaluate RAG pipeline design choices, understand the implications of model selection, and participate in evaluation framework design.
- Domain experience in financial services — investment research, portfolio management, operations, or risk — is a strong advantage given the need to deeply understand the workflows this role will be building for.
- Hands-on experience building and deploying workflows using platforms such as Microsoft Power Platform, Copilot Studio, Anthropic Claude, or equivalent agentic AI tooling; familiarity with prompt engineering and agent configuration is a strong advantage.
Skills and capabilities
- Structured problem framer: able to take ambiguous business problems and decompose them into clearly scoped product opportunities with defined success criteria and realistic scope boundaries.
- Evaluation-minded: understands that AI product quality is measured differently from conventional software quality, and has experience designing and operating LLM evaluation frameworks (automated evals, human eval panels, red-teaming).
- Stakeholder translator: equally comfortable in a technical architecture discussion with engineers and a strategic discussion with investment professionals — adjusts vocabulary and framing without losing precision.
- Outcome-obsessed: tracks leading indicators (activation, usage depth, task completion) and lagging indicators (business impact, user confidence, error rate trends) as naturally as breathing — does not conflate shipping features with delivering value.
Excellent facilitation and stakeholder engagement skills; comfortable running workshops with senior business leaders as well as hands-on build sessions with individual contributors.