At AIA we've started an exciting movement to create a healthier, more sustainable future for everyone.
If you believe in developing a better tomorrow, read on.
About the Role
We are looking for an AI Engineer with extensive developer skills to design, build, and scale AI-powered digital solutions for the insurance domain across our markets in Asia. You will develop end-to-end products-combining machine learning / GenAI capabilities with robust web applications, APIs, and cloud-native architecture-to improve customer journeys, underwriting, claims, fraud detection, servicing, and operational efficiency.
You will collaborate closely with product owners, actuaries, claims assessors, customer service agents, data teams, risk/compliance, and engineering squads to deliver production-ready solutions that are secure, compliant, and measurable.
Build AI and GenAI Capabilities for Insurance Use Cases
- Design and implement AI/ML models and/or GenAI workflows for business use cases, including:
- Claims automation and triage, fraud detection, document intelligence (OCR/extraction), customer service copilots, agent assist, underwriting decision support, next-best-action, and knowledge search.
- Build and improve LLM-powered systems (where applicable), including RAG (Retrieval-Augmented Generation), summarization, classification, and orchestration patterns.
- Establish AI quality and evaluation approaches: offline evaluation prompt/model testing regression checks drift monitoring human-in-the-loop review drift-based retraining triggers retraining pipelines periodic recalibration
- Support AI/ML solutions go-live integration
2) Full Stack Product Engineering (End-to-End Delivery)
- Develop user-facing apps (customer, agent, and operations) and internal tools to operationalize AI outcomes.
- Build secure and scalable backend services, integrating AI capabilities into business workflows.
- Implement authentication/authorization, session management, and role-based access controls for regulated environments.
- Design and build the technical architecture to deploy and maintain AI/ML/Agentic AI in production
- Build and manage ML pipelines (training, evaluation, deployment) with reproducibility and automation.
- Implement monitoring for latency, cost, quality metrics, and model/app performance set up alerting and safe rollback plans.
- Test and optimize machine learning models and algorithms before they go in production
- Ensure solutions follow secure coding practices, identity and access management standards, and encryption requirements.
- Apply privacy controls (masking, minimization, anonymization/pseudonymization) and support audit/risk requirements common in insurance.
- Maintain documentation for model/app decisions, testing evidence, and operational readiness.
- Act as a bridge between data scientists and ITOps & data engineers & data architects, especially during development and production architecture design and set-up
- Communicate trade-offs and outcomes clearly to technical and non-technical stakeholders.
- Mentor junior engineers and contribute to engineering best practices (code reviews, documentation, standards).
