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 Engineer Associate is an early-career engineering role within Singlife's AI Platform Engineering team, responsible for building, testing, and supporting AI-powered solutions on AWS.
Working as part of the AI Centre of Excellence, you will contribute to the delivery of Generative AI, Agentic AI, and Document Intelligence solutions while following established engineering standards, security requirements, and governance controls.
You will gain hands-on experience developing AI applications using AWS services such as Amazon Bedrock, AgentCore, OpenSearch, Textract, Lambda, and API Gateway. The role provides an opportunity to build practical skills in AI engineering, software development, cloud technologies, and enterprise AI delivery.
Responsibilities
AI Solution Build
- Build AI applications, APIs, and reusable components using AWS AI and cloud-native services.
- Develop Generative AI capabilities such as prompt-based applications, retrieval solutions, and AI workflow automation.
- Support the development of Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources.
- Build document processing solutions using OCR and document intelligence technologies.
- Integrate AI solutions with enterprise systems, APIs, and business applications.
- Develop and maintain Python services following engineering standards and best practices.
AWS Services & Platform
- Work with AWS Bedrock, AgentCore, Textract, Comprehend, OpenSearch and Lambda for AI pipeline components — building within the architecture designed by engineering leads
- Connect AI solution outputs to enterprise APIs and downstream systems — REST API integration, data transformation and output validation
- Apply AWS tagging and cost management practices for AI solutions.
- Collaborate with Cloud Engineering teams to ensure solutions are secure, scalable, and reliable.
Quality & Reuse
- Contribute components and utilities to the AI Technology's shared reuse library — packaging code cleanly so it can be used by others on the team
- Write and maintain documentation for every component you deliver — function docstrings, README files and usage examples
- Participate actively in code reviews — respond to feedback constructively and apply it consistently going forward
- Apply AI engineering controls as directed: PII handling, output validation, content filtering and audit logging
Learning & Development
- Stay updated on developments in Generative AI, Agentic AI, and AWS AI services.
- Participate in technical discussions, design sessions, and knowledge-sharing activities.
- Contribute ideas that improve engineering productivity and solution quality.
- Pursue AWS and AI-related certifications as part of professional development.
Requirements
- 2–3 years of software or AI engineering experience — professional or significant project / internship work building AI/ML systems
- Hands-on Python — able to write clean, structured, testable code independently
- Some experience with AWS services — any combination of Lambda, S3, API Gateway, or AI services (Bedrock, AgentCore, Textract, SageMaker) in a real project
- Understanding of REST API development and consumption — FastAPI, Flask or equivalent
- Familiarity with GenAI concepts: LLMs, RAG, prompt engineering, embeddings and vector stores — at a practical level, not just theoretical
- Version control discipline — Git, branching, pull requests, code review
- Curiosity and genuine interest in AI engineering — you have built things with LLMs, not just read about them
- Experience with AWS Bedrock — model invocation, prompt engineering via API
- Exposure to document processing pipelines — PDF extraction, OCR, structured data from unstructured documents
- Familiarity with vector databases or semantic search — Pinecone, OpenSearch, pgvector or equivalent
- AWS Cloud Practitioner or Developer Associate certification (or studying towards one)
- Experience working in a team with code review, CI/CD and documentation discipline
- Exposure to financial services or regulated data environments
