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”.

Be a Part of Something BIG!

The Senior AI Data and Enterprise Architect/Director is responsible for designing and governing the enterprise-wide architecture that enables trusted data, scalable analytics, and production-grade AI—including machine learning and generative AI— across the organization.

This role aligns business strategy, operating models, and technology platforms to deliver secure, compliant, and cost-effective solutions.

You will define target-state architectures, reference patterns, and roadmaps across data platforms, AI/ML lifecycle (MLOps/LLMOps), integration, security, and governance. You will partner with business leaders, product teams, engineering, security, and risk/compliance to ensure AI and data solutions are reliable, explainable, and aligned to enterprise standards.

Make An Impact By

Enterprise Architecture & Strategy

• Lead end-to-end architecture for enterprise data and AI capabilities aligned to business strategy and measurable outcomes.
• Produce target-state architectures, current-state assessments, and multiyear roadmaps spanning platforms, applications, integration, and operating model.
• Define and maintain reference architectures, standards, and guardrails for data, analytics, AI/ML, GenAI, and integration patterns.
• Facilitate architecture governance: design reviews, standards enforcement, exception handling, and technical decision records.

Modern Data Architecture

• Architect scalable data solutions (e.g., Lakehouse/data warehouse, streaming/event ingestion, semantic/metrics layer).
• Establish data modelling standards (conceptual/logical/physical), including domain-oriented data products and/or data mesh patterns as appropriate.
• Design enterprise-grade metadata management, lineage, Catalog, data quality, MDM, and privacy controls.
• Create patterns for real-time/near-real-time analytics, including event-driven architectures and streaming (e.g., Kafka/Kinesis/Pub Sub).

AI/ML & Generative AI Architecture

• Define architecture for the full AI lifecycle: experimentation, training, deployment, monitoring, and continuous improvement.
• Design MLOps/LLMOps frameworks for reproducibility, CI/CD, feature stores, model registries, drift monitoring, and automated evaluation.
• Architect GenAI solutions (where relevant): RAG, vector search, orchestration, prompt/version management, safety filters, and model evaluation.
• Establish patterns for model governance: approvals, auditability, explainability, bias testing, and model risk controls.

Data Science & Applied ML

• Translate business problems into ML solutions (classification, ranking, nextbest- action, anomaly detection, forecasting, summarization).
• Perform feature engineering for streaming and batch contexts (rolling metrics, aggregations, stateful features).
• Train, evaluate, and tune models; define KPIs and acceptance criteria (latency, precision/recall, lift, business impact).
• Develop and iterate on LLM and agent approaches:

• Prompting and structured context design
• Evaluation harnesses (offline + online)
• Guardrails, grounding, and hallucination reduction

• Design experimentation frameworks (A/B tests, shadow mode, canaries) and model monitoring strategies (drift, bias, stability).

Integration & Platform Architecture

• Define integration patterns across enterprise systems (APIs, events, ETL/ELT, iPaaS) to enable reliable data and AI workflows.
• Collaborate with application and infrastructure architects to align data/AI platforms with enterprise principles (observability, resiliency, cost controls).
• Influence vendor selection and platform decisions; evaluate trade-offs across cloud services, open-source tooling, and commercial platforms.

Leadership & Collaboration

• Act as a trusted advisor to executives and senior stakeholders—translating business goals into technical architecture and investment strategy.
• Mentor architects and engineering teams; drive architecture consistency and capability maturity across the organization.
• Lead cross-functional workshops (capability mapping, domain modelling, solution design, risk reviews).

Skills for Success

• Bachelor’s degree in computer science, Engineering, Information Systems, or related field (Master’s preferred or equivalent experience).
• 10–12+ years of experience in enterprise architecture, data architecture, and/or platform architecture.
• Proven experience designing and implementing modern data platforms (Lakehouse/warehouse, ingestion, transformation, governance).
• Strong understanding of AI/ML deployment architectures, including MLOps concepts (CI/CD, monitoring, reproducibility, model registry).
• Experience with cloud platforms (AWS/Azure/GCP) and cloud-na
  
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