Head of AI Data Engineering

Morgan McKinley; Singapore; On-site; SGD 250,000 - 450,000; Full time

Job summary

Unknown telecom leader seeks a deep-tech, hands-on AI/data engineering leader to head its AI-Ready Data & Harness Engineering pillar within a enterprise AI & data transformation initiative. Reporting to senior AI executive, this role owns design, build, run, and continuous improvement of AI-ready data products, knowledge assets, and governance across the enterprise.

The incumbent will drive strategy, build reusable data products, and establish standards for data contracts, privacy-by-design, and

Qualifications

• 10+ years in enterprise data engineering with AI/ML data foundations and governance.
• Led and scaled a data/AI engineering organization (30-50+ engineers).
• Proven track record of reusable data products adopted across production AI programs.
• Able to operate at ExCo-level while reviewing hands-on technical designs.
• Strong ROI discipline tying data decisions to adoption and cost-to-serve.
• Experience coordinating across IT, security, governance in regulated environments.
• Built AI-ready, reusable data products for AI/agent consumption, not data lakes.

Responsibilities

• Own AI-ready data and harness engineering strategy and roadmap.
• Translate AI ambitions into reusable data products and context assets.
• Set standards for data contracts, semantic consistency, and privacy by design.
• Lead a cross-functional team of data product, knowledge, and governance engineers.
• Own the portfolio of AI-ready data products across business units.
• Partner with IT, Cyber, legal, and business teams to accelerate governance.

Skills

Data engineering leadership

AI/ML data foundations

MLOps/LLMOps integration

Governance

ExCo-level communication

Strategic planning

Job description

A leading telecommunications company is seeking a deep-tech, hands-on AI/data engineering leader to head its AI-Ready Data & Harness Engineering pillar within a newly established enterprise AI & data transformation initiative. Reporting directly to the organization's most senior AI/data executive, this role owns the design, build, run, and continuous improvement of AI-ready reusable data products, knowledge/context assets, agent memory capabilities, retrieval/grounding harnesses, and AI data-readiness governance across the enterprise. This pillar is positioned as the foundational data layer that multiplies AI returns — building common data products, semantic context, and retrieval/memory patterns once for reuse across agents, models, journeys, and business units.

• Own the AI-ready data and harness engineering strategy, technical roadmap, and capability architecture, aligned to the enterprise AI stack and senior AI executive's agenda.
• Operate as a top technology leader (CXO-1 level), co-owning decisions on data investments, architecture, knowledge/context engineering, governance, and trade-offs.
• Translate enterprise AI ambition into reusable data products, knowledge assets, context/memory capabilities, retrieval harnesses, evaluation assets, and delivery playbooks, designed for build-once, deploy-many reuse.
• Set standards for data product design, data contracts, semantic consistency, trusted context, privacy/security-by-design, and measurable business value.
• Lead a technical FTE organization spanning AI-ready data products, Knowledge Engineering, Context Engineering, Agent Memory Management, and AI Data Readiness Governance; build and coach data product engineers, data architects, knowledge engineers, ontology/semantic architects, RAG/context engineers, memory engineers, and governance specialists.
• Own the portfolio of reusable AI-ready data products across all business units; define productization standards (data contracts, APIs, metadata, quality thresholds, lineage, access controls, SLAs, lifecycle); track adoption, freshness, quality, cost-to-serve, and business value.
• Own Knowledge Engineering capabilities — ontology, taxonomy, entity resolution, master/reference data alignment, business glossary, knowledge graphs, and semantic layers — ensuring consistent definitions across models, agents, and dashboards.
• Own Context Engineering and retrieval harnesses — chunking, embeddings, vector stores, graph retrieval, hybrid search, ranking, prompt/context packaging, caching — with evaluation datasets, grounding checks, and regression tests.
• Own Agent Memory Management patterns (short/long-term, user/session/entity memory), including write/read policies, retention, privacy, and safety controls.
• Diagnose context and retrieval failures with data scientists and agent engineers; partner with AI/Agent Ops on production telemetry, incident response, and re-indexing.
• Own AI Data Readiness Governance — quality, discoverability, lineage, provenance, privacy, consent, retention, auditability — and define certification gates for experimentation through production scale-up.
• Partner with data owners, IT/CIO, Cyber/CISO, legal, and business teams to make governance an AI delivery accelerator.
• Deliver high-priority data products and harness capabilities in partnership with business teams; ensure services are secure, scalable, observable, and maintainable without accumulating data debt.
• Coordinate across IT, Cyber, data owners, vendors, and hyperscalers to co-solve emerging patterns while avoiding premature lock-in; crash critical paths and build operating rhythms for prioritization and governance review.

Requirements

• 10+ years in enterprise data engineering, with recent depth specifically in AI/ML data foundations, MLOps/LLMOps integration, and AI data governance.
• Has led and scaled a data/AI engineering organization (ideally 30-50+ engineers) spanning data product, knowledge, context/RAG, and governance functions.
• Track record of data products/context assets being adopted by multiple production AI or agent programs, with measurable reuse and value.
• Comfortable operating at both ExCo-level trade-offs and hands-on technical design review.
• Strong commercial/ROI discipline — can tie data and harness decisions to adoption, EBIT, and cost-to-serve outcomes.
• Experience coordinating across IT, Cyber, data governance, and vendor ecosystems in a regulated enterprise environment.
• Has built AI-ready, reusable data products for AI/agent consumption — not primarily data lakes, warehouses, BI, or reporting platforms.
• Deep, hands-on experience in the data/context layer for GenAI and agents: unstructured data, retrieval, embeddings/vector stores, knowledge/context engineering, real-time context assembly, and grounding.
• Has designed engineering frameworks or harnesses that make data consistently consumable across multiple AI/agent applications — not pipelines built for individual analytics use cases.