Head of AI Data Engineering

About the Role

A prominent telecommunications firm in Singapore is looking for a seasoned, technically proficient AI and data engineering leader to head their AI-Ready Data & Harness Engineering division as part of an enterprise-wide AI and data transformation initiative. This pivotal position reports to the organization's senior-most AI and data executive and is responsible for crafting, operating, and enhancing AI-ready reusable data products, knowledge and context assets, agent memory solutions, retrieval systems, and enterprise AI data governance. The division serves as the core data foundation that amplifies AI capabilities across multiple agents, models, customer journeys, and business units through reusable data products and semantic context mechanisms.

Key Responsibilities

• Lead and define the strategy, technical roadmap, and architecture for AI-ready data and harness engineering, aligning closely with the enterprise AI stack and senior leadership objectives.
• Function as a senior technology leader, collaborating on decisions regarding data infrastructure investments, architecture, knowledge engineering, governance, and balancing trade-offs.
• Translate enterprise AI goals into tangible reusable data products, knowledge resources, retrieval and memory capabilities, evaluation tools, and repeatable delivery methods designed for extensive reuse.
• Establish and enforce standards in data product design, semantic consistency, data contracts, trusted contextual information, privacy, and security, targeting measurable business outcomes.
• Lead a technical team encompassing data product engineers, data architects, knowledge and ontology engineers, retrieval augmented generation (RAG) specialists, memory engineers, and governance experts; nurture and grow the team capabilities.
• Oversee the portfolio of reusable AI-ready data products across all business units, managing standards for APIs, metadata, quality controls, lineages, access policies, service levels, and lifecycle governance, while monitoring adoption and value metrics.
• Manage Knowledge Engineering including ontology, taxonomy, entity resolution, master/reference data alignment, business glossaries, knowledge graphs, and semantic layers to maintain semantic uniformity across various AI assets.
• Direct Context Engineering and retrieval harnesses including chunking, embeddings, vector stores, graph retrievals, hybrid search mechanisms, ranking, prompt/context packaging and caching, integrating evaluation datasets and regression testing.
• Administer Agent Memory Management frameworks covering short and long-term memory for users, sessions, and entities, including policies for data write/read, retention, privacy, and safety controls.
• Collaborate with data scientists and agent engineers to troubleshoot context and retrieval issues; partner with AI and agent operations teams on production telemetry, incident responses, and re-indexing efforts.
• Own AI Data Readiness Governance ensuring quality, discoverability, lineage tracking, provenance, privacy compliance, consent management, data retention policies, auditability, and certification gates from experimentation to scaled deployment.
• Work closely with data owners, IT/CIO, security teams (Cyber/CISO), legal, and business divisions to embed governance as an enabler for accelerated AI delivery.
• Deliver high-impact data products and retrieval harnesses in partnership with business stakeholders, ensuring solutions are secure, scalable, observable, and maintainable without accruing technical debt.
• Coordinate efforts among IT, Cybersecurity, data owners, vendors, and cloud providers to address emerging architectural patterns, avoid vendor lock-in, and establish governance and prioritization routines.

Candidate Requirements

• A minimum of 10 years' experience in enterprise data engineering, with recent specialization in AI/ML data foundations, MLOps/LLMOps integrations, and AI data governance frameworks.
• Proven leadership experience managing and scaling a data or AI engineering team of 30-50 or more professionals across data product development, knowledge engineering, context and retrieval engineering, and governance.
• Demonstrated success in delivering reusable data products and contextual assets that have been widely adopted across multiple AI or agent-based production initiatives, with clear measurable benefits.
• Comfortable engaging at executive decision levels for trade-offs while also participating in detailed technical design reviews.
• Strong commercial acumen, able to connect data and engineering choices to business adoption rates, profitability (EBIT), and operational cost metrics.
• Experienced in collaborating across IT, Cybersecurity, governance, and third-party vendors in large regulated enterprises.
• Hands-on expertise in building AI-ready reusable data products optimized for AI and agent consumption distinct from traditional data lakes, warehouses, BI, or analytics platforms.
• Deep technical understanding and practical work with GenAI and agent data layers involving unstructured data, retrieval methods, embeddings/vector storage, knowledge and context engineering, real-time context assembly, and grounding strategies.
• Experience in developing engineering frameworks that enable consistent data consumption across various AI and agent applications rather than bespoke pipelines for individual analytics uses.