Full-timeTech & engineering· Posted 28 Sept

Senior Associate/Assistant Vice President, AI Data Engineer

TemasekFrom company site

Salary

Not stated by the company

When

Full-time hours

Where

NSNECCDhoby GhautSG, 238891, Singapore

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Start: Not stated
Work mode: On-site
Experience: Some experience

About the role

You will build the data foundations that make Temasek's agentic AI systems trustworthy and accurate, working at the intersection of data engineering and AI systems engineering. The role covers structured investment data, unstructured documents and real-time streams, and suits an experienced data engineer who wants to shape how AI agents retrieve and reason over enterprise data.

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What you'll do

Design and build data architectures optimised for AI agent consumption, including structured stores accessed through tool-calling APIs, vector knowledge bases, graph databases and hybrid retrieval systems.
Build and maintain the data layer for RAG pipelines: document ingestion, chunking strategy, embedding generation and refresh, metadata tagging and vector index management across knowledge domains.
Define ontology and schema standards so AI-accessible data assets are consistent, documented and interpretable without custom parsing per product.
Architect real-time and near-real-time feeds for AI agents, covering market data, news and research ingestion, portfolio event streaming and alert-triggering pipelines with defined latency and freshness SLAs.
Define and implement data quality standards such as completeness checks, consistency validation, freshness monitoring and anomaly detection, with automated quality gates.
Build and maintain data lineage tracking across AI data pipelines for traceability, operational debugging and regulatory audit.
Work with the AI Security & Governance Lead and enterprise data governance to ensure AI data assets meet classification, access control and cross-border handling rules, especially for China-facing workflows.
Design and operate data observability tooling including pipeline health monitoring, drift detection, schema change alerting and SLA dashboards.
Conduct regular data quality reviews with AI product teams to identify gaps limiting AI performance and prioritise engineering investment.
Build reusable data assets and services such as a shared investment knowledge graph, company and market data API layer, document intelligence pipeline and portfolio analytics data service.
Maintain a data catalogue for AI-accessible assets covering sources, schemas, freshness, quality metrics, access procedures and limitations.
Contribute to enterprise data platform strategy from an AI-first perspective, advocating for architecture that serves AI consumption rather than only BI and reporting.
Engage external data vendors and market data providers to evaluate, onboard and maintain sources, including quality assessments and licensing coordination with procurement.

Requirements

4–8 years of data engineering experience, with at least 2 years building data infrastructure for AI, ML or LLM systems in production
Experience in a data-intensive organisation with complex, heterogeneous data environments such as financial data or enterprise data platforms
Exposure to investment data domains such as company financials, market data or portfolio systems
Hands-on production experience with RAG pipelines or AI knowledge bases, including vector store management, embedding pipeline design and chunking optimisation
Strong data engineering fundamentals: pipeline design and orchestration, schema design, data quality frameworks and lineage tracking
Python skills including pandas, Polars and SQLAlchemy

Senior Associate/Assistant Vice President, AI Data Engineer

About the role

What you'll do

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