Head of AI Engineering

Purpose

Lead Chubb Life's AI engineering function globally - owning the AI platform, delivering production AI solutions, and setting engineering standards across 8 Asian markets. This role spans GPU infrastructure and LLM services through to end-to-end AI application delivery.

Responsibilities

AI Platform and Infrastructure

• Owns designing, provisioning, integration & GPU compute, LLMs (Azure OpenAI, Anthropic/Claude), vector databases, model registries, and AI developer tooling (Claude Code, GitHub Copilot).

• Build and operate LLMOps/MLOps pipelines covering deployment, versioning, monitoring, and rollback for both generative and traditional ML models.

• Govern platform reliability, security, and cost efficiency across Azure, Databricks, and GPU workloads.

AI Solution Delivery

• Own end-to-end engineering delivery of Chubb Life's AI applications - including the Smart Agency AI suite (Talent Scout, Sales Pitch Advisor, Personal Admin, Elite Coach, Customer Steward, Career Mentor) and all future AI products.

• Translate model specifications and use case designs from Analytics into production-grade applications: agentic systems, RAG pipelines, scoring APIs, and cognitive assistants.

• Apply engineering best practices: CI/CD, observability, testing, and security by design.

Engineering Standards and Enablement

• Define and govern AI engineering standards, architectural patterns, and responsible AI guardrails across Chubb Life globally.

• Drive adoption of modern AI engineering practices: agentic frameworks, RAG architectures, prompt pipelines, and multi-modal AI.

• Enable business teams and developers to use AI tools safely and at scale.

Team Leadership

• Lead and coach a team of AI engineers - setting technical direction, reviewing designs, and elevating team craft.

• Define hiring profiles, team structure, and career pathways for the AI engineering function.

• Establish clear handoff protocols with the Head of Analytics: receive validated model artifacts and use case specifications; own packaging through to production.

Stakeholder and Delivery Management

• Partner with business sponsors and Analytics to scope, prioritise, and deliver high-impact AI initiatives.

• Communicate engineering decisions, risks, and trade-offs to senior stakeholders in clear business terms.

• Track and report on delivery velocity, platform reliability, and the business impact of AI engineering initiatives.

• 12-15+ years in software or AI/ML engineering; 4+ years leading AI/ML teams at scale.

• Hands-on expertise in LLM/GenAI engineering: prompt pipelines, RAG architectures, agentic frameworks, and fine-tuning workflows.

• Experience with GPU infrastructure, model serving platforms, and LLMOps tooling.

• Strong command of Azure, Azure OpenAI, Databricks, and cloud-native AI services.

• Track record of shipping production AI applications in regulated industries (insurance or financial services preferred).

• Strong engineering leadership, sets direction, builds culture, and coaches without micromanaging.

• Bachelor's or master’s in computer science, Engineering, or a related technical field.