Sr. AI Engineer – Agentic Systems

Senior AI Engineer Agentic Systems

The Role

As a Senior AI Engineer on agentic systems youll own the agent architectures that put our models in front of business users and the work of making them reliable enough to stay there. Design agentic behaviour and performance optimization are all inherent to the role because the needs converge: architecture shapes behaviour and behaviour determines what needs optimizing.

What makes it interesting is what the agents have to do. Underwriting claims and the other core areas each involve multi-step workflows with real consequences so agents need to reason over long context follow complex instructions reliably and integrate with systems of record that were never designed for them. Reliability is the hard part and it is largely an evaluation problem rather than a prompting one.

This work is aimed at direct implementation. People on this team move between agentic work systems engineering and applied science as priorities shift. Less a multi-agent system than one generalist with broad tool access.

Major Responsibilities

• Design and develop agentic systems end-to-end: agent loop design orchestration memory and state tool integrations and multi-agent workflows

• Make agents reliable: diagnose why a loop stalls why a tool gets misused or why behaviour drifts between runs and design the guardrails that hold up under real traffic

• Measure agent quality continuously rather than at demonstration time: evaluation sets that reflect real workflows regression checks that catch behavioural drift and analysis that explains a failure rather than only flagging it

• Tune the tradeoffs that decide whether an agent is usable: latency answer quality and token cost including the judgement of which to give up in a given workflow

• Integrate agents with the systems and data they depend on and carry them from prototype to something the business can rely on in production

What Youll Bring

• Deep expertise in production Python with strong working knowledge of current agent frameworks and protocols (Claude Agent SDK deepagents LangGraph MCP or equivalents)

• Experience building and debugging agentic systems in production where failure modes are emergent rather than exceptions: loop instability tool misuse unbounded context growth and non-determinism

• A feel for the performance envelope: where latency comes from in a multi-step agent what a token budget buys and how to trade quality against both

• Evaluation rigour and the judgement to design measurement that holds up in a business environment rather than a demonstration

• Fluency with agentic coding tools in your own workflow such as Claude Code and Codex. The team is fully immersed in this way of engineering and we expect it to be part of how you build rather than something reached for occasionally

• The profile can come from either direction: a software engineer who has moved into AI systems or an AI engineer with strong systems fundamentals. Either way we expect engineering discipline meaning version control tests reproducibility and code the next person can pick up

Strong Preference Given To

• Evaluation and tracing tooling for agent behaviour (LangSmith Weights & Biases or custom solutions)

• Distributed and event-driven systems experience (Kafka or similar)

• Retrieval and memory system design for long-running agents

• Full-stack development experience integrating agents into user-facing applications

About Chubb

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

Were 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 were building the future of intelligent insurance solutions.

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 applicants accessibility needs.