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.
Responsibilities
AI Platform and Infrastructure
- Design, provision, integrate, and manage 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 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.
Stakeholder and Delivery Management
- Partner with business sponsors and Analytics to scope, prioritize, 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.