Director, AI, Data & Enterprise Architecture

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 sayHello 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 AIincluding 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 stakeholderstranslating 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

• Bachelors degree in computer science Engineering Information Systems or related field (Masters preferred or equivalent experience).
• 1012 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-native architecture (containers Kubernetes serverless IaC).
• Strong grasp of security privacy and governance principles for data and AI systems.
• Ability to communicate complex architecture to technical and non-technical audiences and drive alignment across teams.

Rewards that Go Beyond

• Flexible work arrangements
• Full suite of health and wellness benefits
• Ongoing training and development programs
• Internal mobility opportunities

Are you ready to say hello to BIG Possibilities

Take the leap with Singtel to unlock new opportunities and accelerate your growth. Apply now and start your empowering career!

We are committed to a safe and healthy environment for our employees & customers and will require all prospective employees to be fully vaccinated.