{"id":1232997,"url":"https://alion.io/job/singtel-senior-director-ai-ready-data-harness-engineering","title":"Senior Director, AI-Ready Data & Harness Engineering","company":{"id":49912,"name":"Singtel","domain":"singtel.com","url":"https://alion.io/company/singtel","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"A","score":100,"open_postings":5,"ghost_share":0,"stale_share":0.2,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":65000,"max_usd":165000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":112},"experience_years_min":20,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AgentOps","optional":false},{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"Embeddings","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLMOps","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false}],"status":"live","first_seen_at":"2026-09-10T00:00:00Z","employer_posted_date":"2026-09-10","last_verified_at":"2026-10-20T22:03:48Z","board_verified":true,"closed_at":null,"days_open":41,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":41},"description":"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”.\nA 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.\nAt 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.\nMake an impact by:\nAI-Ready Data & Harness Strategy and CXO-1 Deep-Tech Accountability\nOwn 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.\nOperate 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.\nTranslate enterprise AI ambition into reusable data products, knowledge assets, context/memory capabilities, retrieval harnesses, evaluation assets, data standards and delivery playbooks.\nTreat 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.\nSet the bar for AI-ready data product design, data contracts, semantic consistency, trusted context, privacy/security-by-design, operational reliability and measurable business value.\nEnsure Singtel's AI investments are constrained by value and quality - not by data fragmentation, unclear ownership, weak governance, stale context or brittle retrieval patterns.\nBuild and Own the AI-Ready Data Engineering Powerhouse\nLead Tech FTE organization across AI-ready reusable data products, Knowledge Engineering, Context Engineering, Agent Memory Management and AI Data Readiness Governance.\nBuild 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.\nCreate a culture of product ownership, reusable design, engineering depth, data quality, governance-by-design, production reliability, cost discipline and business-value accountability.\nUse partners and specialist vendors selectively while retaining ownership of critical data IP, architecture, standards, semantic definitions, governance, retrieval quality and delivery accountability.\nRaise the expertise level of existing data-readiness capabilities and rationalize fragmented or duplicated work into stronger, accountable sub-functions.\nReusable AI-Ready Data Products for All BUs\nOwn 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.\nDefine productization standards for data domains, data contracts, APIs, metadata, discoverability, ownership, quality thresholds, lineage, access controls, SLAs/SLOs, lifecycle and retirement.\nConvert fragmented datasets, documents, events and operational signals into certified, reusable assets that AI / ML, GenAI and Agentic AI teams can consume through stable interfaces.\nPrioritize high-value data products that accelerate multiple AI use cases rather than one-off pipelines for isolated pilots.\nDesign 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.\nTrack adoption, reuse, freshness, quality, cost-to-serve, defect rates, time-to-integrate and business value contribution for each major data product.\nKnowledge Engineering, Ontology, Knowledge Graphs and Semantic Layer\nOwn Knowledge Engineering capabilities (with federated operational structure) including ontology, taxonomy, entity resolution, master/reference data alignment, business glossary, knowledge graphs and semantic layers.\nBuild enterprise intelligence assets that link customer, product, service, network, device, order, ticket, interaction, finance, campaign, process and policy knowledge where relevant for AI value.\nEnsure semantic consistency across BUs so models, agents, dashboards and decision services use common definitions, relationships and trusted business meaning.\nEstablish 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.\nPartner with AI Capabilities & Services, Central AI Kitchen and BU teams to expose knowledge assets through APIs, graph queries, semantic services and reusable context packages.\nDesign knowledge assets for explainability, auditability, policy enforcement, search/retrieval quality and responsible AI use.\nContext Engineering, Retrieval Harnesses and Agent Memory Management\nOwn 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.\nBuild reusable RAG and retrieval harnesses with evaluation datasets, gold-standard answers, grounding checks, retrieval-quality metrics, regression tests, traceability and improvement loops.\nOwn 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.\nEnsure 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.\nDiagnose 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.\nPartner 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.\nAI Data Readiness Governance, Trust and Policy-by-Design\nOwn AI Data Readiness Governance for AIDA, covering quality, discoverability, lineage, provenance, policy, ownership, access, classification, privacy, consent, retention and auditability.\nDefine 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.\nPartner 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.\nImplement governance-by-design in data products, knowledge assets, retrieval stores, agent memory, logs, feedback data and model/agent evaluation datasets.\nMaintain clear policies for sensitive data, customer data, operational data, third-party data, generated data, human feedback, agent traces and derived intelligence assets.\nProduction Data and Harness Delivery Without Data Debt\nDevelop and operationalize high-priority AI-ready data products, knowledge/context assets and harness capabilities with AIDA Business Partners and business teams.\nConvert ambiguous business problems into data product designs, source-system/integration requirements, semantic models, retrieval architectures, governance plans, adoption paths and measurable outcomes.\nEmbed AI-ready data and harness capabilities into Central AI Kitchen, AI Services & Capabilities, AI & Agent Ops, business workflows, enterprise systems and decision processes.\nEnsure data and context services are secure, scalable, observable, testable, resilient, cost-efficient, maintainable and supportable by Day 2 operations.\nMake 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.\nEcosystem Orchestration, Critical Path Acceleration and Engineering Culture\nCoordinate 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.\nCrash 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.\nBuild 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.\nTranslate technical data and harness trade-offs into clear executive choices while retaining credibility with expert data engineers, AI engineers, architects, cyber teams and SMEs.\nSkills for Success:\nA senior deep-tech AI-ready data and harness engineering leader with:\n20+ 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.\nExperience 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.\nDirect 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.\nStrong 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.\nHands-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.\nTrack record delivering large, multi-stakeholder AI / data / digital programs from strategy through production launch, adoption, operations and measurable value realization.\nOwnership of ROI, investment cases, data product economics, reuse targets, adoption metrics, productivity outcomes, EBIT contribution and technical/data debt management.\nExperience coordinating across AIDA, IT, Cyber, DPM/data governance, data owners, platform teams, product, business, vendors and partners in regulated enterprise ecosystems.\nProven ability to identify new opportunities, prioritize fewer/bigger bets, crash critical paths and unblock delivery without compromising architecture, governance, security, reliability or maintainability.\nEnterprise data engineering and data product architecture across batch/stream pipelines, APIs, data contracts, data quality, metadata, lineage, ownership, observability, lifecycle and SLA/SLO management.\nAI-ready reusable data products across customer, product, service, network, 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