Head of AI Engineering
Chubb Open · verified Nov 1, 2026 At a glance
Director-level AI Engineering role at Chubb. Hong Kong · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Run the AI platform across GPU compute, language models, vector databases, model registries and developer tools
- Build model operations pipelines for deployment, version tracking, monitoring and rollback across generative and traditional ML
- Deliver AI applications from model specifications, including agentic systems, retrieval pipelines, scoring APIs and cognitive assistants
- Set engineering standards and responsible AI safeguards, while coaching teams to apply modern AI practices safely
- Lead AI engineers, coordinate delivery with Analytics and business sponsors, and report on reliability, pace and business impact
What you bring
- At least twelve years in software or AI/ML engineering, including four years leading AI/ML teams at scale
- Hands-on work with LLM and generative AI engineering, including prompt pipelines, retrieval, agent frameworks and fine-tuning
- Experience with GPU infrastructure, model serving and LLM operations, plus strong Azure and Databricks skills
- A record of launching production AI applications in regulated sectors; insurance or financial services experience is preferred
- A bachelor's or master's degree in computer science, engineering or a related technical discipline
Who this fits
This director-level role suits an experienced engineering leader who can set technical direction and coach a team while remaining hands-on with AI systems. You’ll lead work across Chubb Life’s AI engineering function globally, spanning eight Asian markets.
From the employer
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.
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