Senior Director, AI-Ready Data & Harness Engineering

Singtel Group; Singapore; On-site; SGD 360,000 - 520,000; Full time

Job summary

Singtel Group in Singapore seeks a Senior Director to lead the AI-Ready Data & Harness Engineering pillar within the AIDA 2.0 program. This role owns the design, build, run and governance of AI-ready reusable data products, knowledge assets, and memory/harness capabilities at scale, reporting directly to the Chief AIDA Officer.

You will shape the data architecture, drive data contracts and semantic consistency, and enable cross-BU reuse across models, agents and journeys to maximize AI returns

Qualifications

• Deep-tech, hands-on AI/data engineering leader
• Experience leading AI-ready data, knowledge/context assets, and governance
• Ability to design and implement reusable data products and data contracts

Responsibilities

• Own AI-Ready Data & Harness Engineering strategy, roadmap and architecture
• Lead senior decisions on AI-ready data investments, data contracts and governance
• Translate enterprise AI ambitions into reusable data products and memory capabilities
• Design for build-once, deploy-many reuse across models, agents and BUs
• Set standards for data accuracy, privacy, security and measurable business value
• Build and mentor a team across data products, ontology, context engineering and governance

Job description

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Senior Director, AI-Ready Data & Harness Engineering

An empowering career at Singtel begins with a Hello. Our purpose, to Empower Every Generation, connects people to the possibilities they need to excel. Every "hello" at Singtel opens doors to new initiatives, growth, and BIG possibilities that takes your career to new heights. So, when you say hello to us, you are really empowered to say…“Hello BIG Possibilities".

A deep-tech, hands-on AI/data engineering leader to head Singtel's AI-Ready Data & Harness Engineering pillar in AIDA 2.0. Reporting directly to the Chief AIDA Officer, 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 at Singtel scale.

At Singtel scale, this pillar is the foundational AI data layer that creates the multiplier effect in AI returns: common data products, semantic context, retrieval and memory patterns should be built once and deployed multiple times across agents, models, journeys and BUs.

Make an impact by:

AI-Ready Data & Harness Strategy and CXO-1 Deep-Tech Accountability

• Own the AI-Ready Data & Harness Engineering strategy, technical roadmap and capability architecture for Singtel SG, aligned to the AIDA 2.0 stack and Chief AIDA Officer agenda.
• Operate as a CXO-1 technology leader: shape, challenge and co-own senior decisions on AI-ready data investments, data architecture, knowledge/context engineering, governance, sequencing, technical trade-offs and outcomes.
• Translate enterprise AI ambition into reusable data products, knowledge assets, context/memory capabilities, retrieval harnesses, evaluation assets, data standards and delivery playbooks.
• Treat the AI-ready data and harness layer as the foundational multiplier for AIDA returns, explicitly designing for build-once, deploy-many reuse across models, agents, channels and BUs.
• Set the bar for AI-ready data product design, data contracts, semantic consistency, trusted context, privacy/security-by-design, operational reliability and measurable business value.
• Ensure Singtel's AI investments are constrained by value and quality - not by data fragmentation, unclear ownership, weak governance, stale context or brittle retrieval patterns.

Build and Own the AI-Ready Data Engineering Powerhouse

• Lead Tech FTE organization across AI-ready reusable data products, Knowledge Engineering, Context Engineering, Agent Memory Management and AI Data Readiness Governance.
• Build and coach high-calibre data product engineers, data engineers, data architects, knowledge engineers, ontology/semantic architects, context/RAG engineers, memory engineers, governance specialists and AI data operations talent.
• Create a culture of product ownership, reusable design, engineering depth, data quality, governance-by-design, production reliability, cost discipline and business-value accountability.
• Use partners and specialist vendors selectively while retaining ownership of critical data IP, architecture, standards, semantic definitions, governance, retrieval quality and delivery accountability.
• Raise the expertise level of existing data-readiness capabilities and rationalize fragmented or duplicated work into stronger, accountable sub-functions.

Reusable AI-Ready Data Products for All BUs

• Own the portfolio of reusable AI-ready data products across Consumer, Enterprise, Network & IT and corporate functions, aligned to the AIDA 2.0 org expectation for all BUs.
• Define productization standards for data domains, data contracts, APIs, metadata, discoverability, ownership, quality thresholds, lineage, access controls, SLAs/SLOs, lifecycle and retirement.
• Convert fragmented datasets, documents, events and operational signals into certified, reusable assets that AI / ML, GenAI and Agentic AI teams can consume through stable interfaces.
• Prioritize high-value data products that accelerate multiple AI use cases rather than one-off pipelines for isolated pilots.
• Design priority data products for build-once, deploy-many scale, maximizing reuse across BUs and AI journeys instead of creating bespoke pipelines or duplicated domain assets.
• Track adoption, reuse, freshness, quality, cost-to-serve, defect rates, time-to-integrate and business value contribution for each major data product.

Knowledge Engineering, Ontology, Knowledge Graphs and Semantic Layer

• Own Knowledge Engineering capabilities (with federated operational structure) including ontology, taxonomy, entity resolution, master/reference data alignment, business glossary, knowledge graphs and semantic layers.
• Build enterprise intelligence assets that link customer, product, service, network, device, order, ticket, interaction, finance, campaign, process and policy knowledge where relevant for AI value.
• Ensure semantic consistency across BUs so models, agents, dashboards and decision services use common definitions, relationships and trusted business meaning.
• Establish trusted single-ledger semantics for customer, product, employee, network and operational entities, especially where UDP and the Network on-prem HP lake do not yet provide an enterprise semantic layer.
• Partner with AI Capabilities & Services, Central AI Kitchen and BU teams to expose knowledge assets through APIs, graph queries, semantic services and reusable context packages.
• Design knowledge assets for explainability, auditability, policy enforcement, search/retrieval quality and responsible AI use.

Context Engineering, Retrieval Harnesses and Agent Memory Management

• Own Context Engineering (with federated