Head of AI Engineering

Job description

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.

Qualifications

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.