Senior Director, AI-Ready Data & Harness Engineering

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A deep-tech hands-on AI/data engineering leader to head Singtels 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 Singtels 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.