Senior Director, AI-Ready Data & Harness Engineering

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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 operational structure) for AI/GenAI/Agentic AI across chunking, embeddings, vector stores, graph retrieval, hybrid search, ranking, prompt/context packaging, caching, tool data interfaces and context-window economics.
• Build reusable RAG and retrieval harnesses with evaluation datasets, gold-standard answers, grounding checks, retrieval-quality metrics, regression tests, traceability and improvement loops.
• Own Agent Memory Management patterns for short-term and long-term memory, user/session/entity/process memory, memory write/read policies, retention, privacy, explainability and safety controls.
• Ensure agents consume consistent governed context and memory so hundreds of agents do not form divergent customer/product understanding; optimize context availability, freshness and retrieval paths for low-latency customer experience.
• Diagnose context and retrieval failures with data scientists, agent engineers and business SMEs, including stale sources, missing entities, poor chunking, weak metadata, bad rankings, hallucination-inducing gaps and latency/cost trade-offs.
• Partner with AI & Agent Ops for production telemetry, feedback loops, incident response, rollback, re-indexing, refresh cycles and post-launch improvement of context and memory capabilities.

AI Data Readiness Governance, Trust and Policy-by-Design

• Own AI Data Readiness Governance for AIDA, covering quality, discoverability, lineage, provenance, policy, ownership, access, classification, privacy, consent, retention and auditability.
• Define certification gates for experimentation, pilot, production launch and scale-up so AI use cases consume trusted, policy-compliant and fit-for-purpose data/context assets.
• Partner with DPM/data owners, IT/CIO, Cyber/CISO, legal/regulatory and business teams to make data governance an accelerator for AI delivery rather than a late-stage blocker.
• Implement governance-by-design in data products, knowledge assets, retrieval stores, agent memory, logs, feedback data and model/agent evaluation datasets.
• Maintain clear policies for sensitive data, customer data, operational data, third-party data, generated data, human feedback, agent traces and derived intelligence assets.

Production Data and Harness Delivery Without Data Debt

• Develop and operationalize high-priority AI-ready data products, knowledge/context assets and harness capabilities with AIDA Business Partners and business teams.
• Convert ambiguous business problems into data product designs, source-system/integration requirements, semantic models, retrieval architectures, governance plans, adoption paths and measurable outcomes.
• Embed AI-ready data and harness capabilities into Central AI Kitchen, AI Services & Capabilities, AI & Agent Ops, business workflows, enterprise systems and decision processes.
• Ensure data and context services are secure, scalable, observable, testable, resilient, cost-efficient, maintainable and supportable by Day 2 operations.
• Make deliberate trade-offs that accelerate value while avoiding brittle one-off pipelines, hidden data debt, unmanaged dependencies, duplicate semantic layers, ungoverned shadow datasets and fragile run operations.

Ecosystem Orchestration, Critical Path Acceleration and Engineering Culture

• Coordinate with AIDA, IT, Cyber, data owners, vendors, hyperscalers and industry partners to co-solve emerging semantic/context/harness patterns while avoiding premature lock-in and preserving speed-to-value.
• Crash critical paths by surfacing data/source-system dependencies early, clarifying ownership, forcing data/architecture/governance decisions, removing blockers and escalating trade-offs at the right level.
• Build operating rhythms for data product prioritization, source-system readiness, governance review, context quality, semantic alignment, cyber review, platform integration, release readiness and post-launch improvement.
• Translate technical data and harness trade-offs into clear executive choices while retaining credibility with expert data engineers, AI engineers, architects, cyber teams and SMEs.

Skills for Success:

A senior deep-tech AI-ready data and harness engineering leader with:

• 20+ years of hands-on experience across enterprise data engineering, data products, analytics platforms, AI/ML data foundations, MLOps/LLMOps data integration, knowledge/context engineering, retrieval systems and AI data governance.
• Experience building and owning enterprise-scale AI-ready data, knowledge/context, data platform or AI-enablement organizations at Singtel scale or higher, ideally 30-50+ data engineers, data product engineers, knowledge engineers, AI platform engineers and governance specialists.
• Direct involvement in technical design and delivery: data architecture, batch/stream pipelines, APIs, data contracts, metadata, quality, lineage, semantic layers, ontologies, knowledge graphs, feature/embedding/vector stores, RAG/retrieval harnesses and agent memory patterns.
• Strong track record creating AI-ready reusable data products and data governance capabilities that are adopted by multiple BUs and production AI/agent programs, not just dashboard/reporting datasets.
• Hands-on performance improvement with data scientists, AI engineers, data engineers and business SMEs, including root-cause analysis of data quality, freshness, semantic ambiguity, retrieval misses, missing context, memory errors, latency, cost and workflow failures.
• Track record delivering large, multi-stakeholder AI / data / digital programs from strategy through production launch, adoption, operations and measurable value realization.
• Ownership of ROI, investment cases, data product economics, reuse targets, adoption metrics, productivity outcomes, EBIT contribution and technical/data debt management.
• Experience coordinating across AIDA, IT, Cyber, DPM/data governance, data owners, platform teams, product, business, vendors and partners in regulated enterprise ecosystems.
• Proven ability to identify new opportunities, prioritize fewer/bigger bets, crash critical paths and unblock delivery without compromising architecture, governance, security, reliability or maintainability.
• Enterprise data engineering and data product architecture across batch/stream pipelines, APIs, data contracts, data quality, metadata, lineage, ownership, observability, lifecycle and SLA/SLO management.
• AI-ready reusable data products across customer, product, service, network, operations, sales, finance and enterprise domains, with strong product management, adoption, reuse and cost-to-serve discipline.
• Knowledge Engineering across ontologies, taxonomies, entity resolution, business glossaries, semantic layers, knowledge graphs, graph querying and enterprise intelligence asset design.
• Context Engineering and RAG/retrieval across chunking, embeddings, vector stores, hybrid search, graph retrieval, ranking, prompt/context packaging, grounding, evaluation datasets and regression testing.
• Agent Memory Management across user/session/entity/process memory, read/write policies, retention, privacy, explainability, security, observability and production improvement loops.
• AI Data Governance & Trust across quality, discoverability, policy, privacy, consent, access control, classification, retention, provenance, auditability, responsible AI and model/data risk.
• Production integration with AIDA stack: AI capabilities, Central AI Kitchen Platform & Ops, AI & Agent Ops, AI Value Realization Office, IT architecture, Cyber, DPM/data owners and BU systems/workflows.
• Executive gravitas to operate as a Chief AIDA Officer direct report and influence CXO-level business, technology, cyber, IT, DPM/data and governance leaders.
• Roll-up-sleeves technical leadership style; comfortable moving from ExCo-level trade-offs into detailed design reviews with data engineers, data product owners, knowledge engineers, AI engineers, platform engineers and cyber teams.
• Decisive prioritization and trade-off capability; able to sharpen focus, crash critical paths and make clear calls under ambiguity.
• Strong ecosystem orchestration across AIDA, IT, Cyber, DPM/data owners, platform, governance, product, business teams, vendors and partners.
• Commercial and ROI discipline; able to connect data/harness choices to adoption, productivity, revenue enablement, EBIT, compute/model economics and cost-to-serve outcomes.
• Talent builder and culture shaper who can attract scarce data/AI engineering talent, raise engineering standards and create a high-accountability AI-ready data engineering organization.
• Ability to balance delivery speed with long-term engineering integrity, avoiding fragile pilots, duplicate data products, unmanaged dependencies, ungoverned datasets and data debt.
• Ability to communicate complex data, governance, retrieval and AI harness trade-offs in business language while maintaining technical credibility with expert teams.