Senior Associate/Assistant Vice President, AI Data Engineer

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
For Temasek Review 2026, please visit
For Sustainability Report 2026, please visit

Introduction

AI agents are only as good as the data they can reason over. Poorly structured, stale, or inconsistently governed data is the most common reason enterprise AI products fail to deliver value — not model capability, but data readiness. The AI Data Engineer at Temasek is responsible for building the data foundations that make Temasek's agentic AI systems trustworthy, accurate, and capable of reasoning over the complex, heterogeneous data environment of a global investment institution.

This role sits at the intersection of data engineering and AI systems engineering — responsible for designing and building the data architectures, pipelines, and quality frameworks that allow AI agents to retrieve, reason over, and act on Temasek's investment data. You will work across structured investment data (portfolio positions, financial statements, market data), unstructured data (research reports, company filings, meeting notes, news), and real-time data streams — making all of it accessible, reliable, and AI-readable.

ResponsibilitiesAgent-ready data architecture

• Design and build data architectures specifically optimised for AI agent consumption: structured data stores accessible via tool-calling APIs, vector knowledge bases for semantic retrieval, graph databases for relationship-based reasoning, and hybrid retrieval systems that combine keyword, semantic, and structured query approaches.

• Build and maintain the data layer for Temasek's RAG (Retrieval-Augmented Generation) pipelines: document ingestion workflows, chunking strategy design, embedding generation and refresh pipelines, metadata tagging for filtered retrieval, and vector index management across multiple knowledge domains (company research, market intelligence, portfolio data, regulatory filings).

• Design the ontology and schema standards for AI-accessible data assets — ensuring that data structures are consistent, well-documented, and interpretable by AI agents without requiring ad-hoc parsing or custom logic in every product that consumes them.