AI Engineer

About Chubb

Chubb is the world's largest publicly traded property and casualty insurer, with operations in 54 countries providing commercial and personal insurance, reinsurance, and life insurance to clients worldwide.

We're at the forefront of AI-driven transformation in insurance.
Chubb is making strategic investments in artificial intelligence to fundamentally change how we assess risk, serve customers, and operate our business. Combining cutting-edge AI capabilities with our exceptional financial strength, comprehensive product portfolio, and global reach, we're building the future of intelligent insurance solutions.

The Role

As a Senior AI Engineer, you'll work with a global team of engineering and product teams to implement state-of-the-art AI-enabled digital solutions transforming P&C insurance globally. This position spans three core disciplines:
applied AI/ML
,
production AI/LLM systems engineering
, and
agentic AI systems
offering the opportunity to work across the full AI product lifecycle from model development to production deployment.

Major Responsibilities

- Work closely with AI engineers and product teams to design, build, and deploy ML/AI solutions solving complex business challenges in cloud environments (CPU & GPU)
- Research & implement state-of-the-art LLM techniques including post-training (e.g., SFT, GRPO, RLHF), prompt engineering, and evaluation frameworks
- Build production AI services
including middleware, prompt management layers, inference pipelines, LLM-Ops tooling, and async messaging systems (e.g., Kafka) for model integration into applications
- Design and develop agentic AI systems
end-to-end from orchestration frameworks (LangChain, CrewAI, AutoGen) to memory/data layers, tool integrations, and multi-agent workflows
- Implement observability, monitoring, and evaluation
for production AI systems including prompt tracing, agent behavior analysis, and performance metrics
- Build reusable pipelines, processes, and tools to streamline AI/ML workflows from development to production
- Ensure high-quality, maintainable code that meets business objectives, quality standards, and development guidelines
- Manage stakeholder expectations and communications while adapting to shifting priorities

Minimum Requirements

Must Have:

• 5+ years' experience in Machine Learning/AI Engineering/Software Engineering/Data Science with deep expertise in production Python development
• Strong knowledge of ML frameworks and libraries (Transformers, trl, PyTorch, LangChain, LlamaIndex, or similar)
• Experience building distributed, high-throughput, low-latency systems
• Experience with containerization and orchestration technologies (Kubernetes, Docker)

Strong Preference Given To:

- Experience with the
full LLM lifecycle
: dataset curation, pos-training, evaluation, data labelling and management, and deployment
- Production AI systems experience
: building APIs, middleware, prompt management, inference optimization, or LLM-Ops infrastructure
- Agentic AI development
: building autonomous agents, multi-agent systems, memory/state management, tool use, or agent evaluation frameworks
- Experience with
observability/monitoring tools
for AI systems (LangSmith, Weights & Biases, custom tracing solutions)
- Understanding of system architecture design and technical feasibility tradeoffs
- Full-stack development experience integrating AI into user-facing applications
- P&C insurance domain knowledge

*Chubb Canada does not use artificial intelligence (AI) tools to assess, screen, or select applicants.*

*At Chubb we are committed to providing equal employment opportunities to all employees and applicants. It is our policy to provide equal employment opportunities to employees and applicants based on job-related qualifications and ability to perform a job. If you require an accommodation during the hiring process or upon hire, please inform Human Resources. If a selected applicant requests accommodation during the recruitment process, Chubb will consult with the applicant in order to provide suitable accommodation that takes into account the applicant’s accessibility needs.*