VP, AI Engineering

Singlife is a leading homegrown financial services company, offering consumers a better way to financial freedom. Through innovative, technology-enabled solutions and a wide range of products and services, Singlife provides consumers coantrol over their financial wellbeing at every stage of their lives.

In addition to a comprehensive suite of insurance plans, employee benefits, partnerships with financial adviser channels and bancassurance, Singlife offers investment and advisory solutions through its GROW with Singlife platform. It also offers the Singlife Account, a mobile-first insurance savings plan.

Singlife is the exclusive insurance provider for the Ministry of Defence, Ministry of Home Affairs and Public Officers Group Insurance Scheme. Singlife is also an official signatory of the United Nations Principles for Sustainable Insurance and the United Nations-supported Principles for Responsible Investment, affirming its commitment to finding a better way to sustainability.

The merger of Aviva Singapore and Singlife was announced in September 2020 and created one of the largest homegrown financial services companies in Singapore in a deal valued at S$3.2 billion. It was the largest insurance deal in Singapore at the time. Singlife was subsequently acquired by Sumitomo Life in March 2024, one of Japan’s leading life insurers, which valued Singlife at S$4.6 billion, making the transaction one of the largest insurance deals in Southeast Asia.

About The Role

The AI Engineering Lead is the technical authority of the AI Platform Engineering. This role owns the end-to-end engineering of all AI solutions — from solution design alongside the Enterprise and Cloud Architects through to production delivery — and is accountable for the technical quality of everything builds or procures.

The role is responsible for defining how Singlife designs, builds, tests, deploys, governs, and operates AI solutions on AWS. This includes Generative AI, Agentic AI, Retrieval-Augmented Generation, document intelligence, LLM integration, AI workflow orchestration, MLOps / LLMOps, and reusable AI engineering standards.

The AI Engineering Lead works closely with Enterprise Architects, Cloud Architects, Cloud Platform Engineers, Data Science & Analytics teams, AI Governance, and delivery teams to ensure AI solutions are secure, scalable, reusable, cost-aware, and aligned to enterprise technology standards.

This is a hands-on leadership role. The AI Engineering Lead is expected to guide solution design, review technical implementation, lead the engineering team, oversee vendor-delivered AI solutions, and establish production-grade engineering discipline across the AI Platform Engineering function.

Responsibilities

End-to-End Solution Design

Own the technical solution design for all AI use cases — from intake through to production — producing architecture artefacts, design decisions and technical specifications
Work alongside the Enterprise and Cloud Architects on end-to-end solution design — ensuring AI solutions are enterprise-compliant, secure and platform-aligned from day one
Produce and review architecture artefacts, technical designs, integration patterns, engineering standards, and implementation plans.
Provide technical input into build / buy / partner decisions by assessing feasibility, complexity, delivery risk, maintainability, cost, and long-term platform fit.
Ensure AI solutions are designed for reuse, operational support, observability, governance, and future extensibility.

AI Platform & Services — Design & Oversight

Own the AI engineering layer on AWS — defining which native services are used for each use case and how they connect to the enterprise stack
Lead technical design across the core AWS AI stack: Bedrock (LLM orchestration, agents, knowledge bases, guardrails), AgentCore, SageMaker (ML model deployment, pipelines, MLOps), Textract/Comprehend (document intelligence), OpenSearch (vector search, RAG retrieval), Step Functions (pipeline orchestration)
Work with the Cloud Platform Engineer — who owns environment provisioning and infrastructure setup — by providing clear technical requirements per use case so they can configure AWS environments correctly
Own the LLMOps and MLOps architecture — model versioning, deployment, monitoring, drift detection, retraining triggers and production observability on AWS
Oversee AWS AI service cost architecture — working with the AI Champion and Cloud Platform Engineer on FinOps tagging, per-use-case cost allocation and spend optimisation

Production Engineering & Standards

Ensure all AI solutions are built to production standards — with appropriate testing, security controls, PII handling, monitoring, logging, resilience, error handlign and operational runbooks
Own the AI Platform Engineering reuse library — ensuring components, patterns, prompt templates and AWS service wrappers built for one use case are packaged and available for reuse
Lead technical production readiness reviews before any AI solution goes live — covering model risk, security, performance, cost and rollback plan
Define standards for CI/CD, automated testing, release management, observability, incident response, and operational support for AI workloads.
Establish and maintain AI engineering runbooks, support models, incident playbooks, and operational standards for live AI solutions.
Work with AI Governance and Risk teams to provide required technical artefacts such as architecture documents, model cards, risk mitigations, control evidence, and production readiness sign-offs.
Ensure governance requirements are embedded into delivery processes without creating unnecessary delivery friction.

Team Leadership

Lead the AI Platform Engineering team across AI engineering, AI solution development, and AI testing disciplines.
Provide technical direction, coaching, and development support to AI Engineers, AI Engineer Associates, and AI Test Engineers.
Set engineering culture and delivery d