Head of AI Engineering

Chubb; Hong Kong; On-site; HKD 1,800,000 - 3,000,000; Full time

Job summary

Chubb Life is seeking a senior AI engineering leader to own the AI platform, deliver production AI solutions, and set engineering standards across 8 Asian markets. You will oversee GPU infrastructure and LLM services, guiding end-to-end AI application delivery.

You will lead a global team of AI engineers, translate model specifications into production-grade applications, and champion CI/CD, observability, and secure design while partnering with Analytics and business sponsors to scale impact

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.

Responsibilities

• Own AI platform and infrastructure design, provisioning, integration, GPU compute, LLMs, vector databases, model registries, and AI developer tooling.
• 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.
• Own end-to-end engineering delivery of Chubb Life's AI applications – including the Smart Agency AI suite 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.
• 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.
• 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.
• 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.

Skills

LLM/GenAI engineering

Prompt pipelines

RAG architectures

Agentic frameworks

Fine-tuning workflows

GPU infrastructure

LLMOps tooling

Azure OpenAI

Education

Bachelor's or Master's in CS/Engineering

Tools

Azure

Azure OpenAI

Databricks

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.

Job Description

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.

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.