Sr. AI Engineer – Agentic Systems

Chubb; Toronto; On-site; CAD 150,000 - 210,000; Full time

Job summary

Chubb Canada is seeking a Senior AI Engineer to own the agent architectures that put our models in front of business users, ensuring reliability and scalable performance in production.

You'll blend agentic systems work with software engineering, building end-to-end pipelines, evaluating behavior, and tuning latency, cost, and quality while collaborating with cross-functional teams and aligning with business needs.

Qualifications

• Production Python expertise with agent frameworks and production-grade systems.
• Experience building and debugging agentic systems where failure modes are emergent (loop instability, non-determinism).
• Ability to measure performance and reliability in business contexts, beyond demonstrations.
• Fluency with agentic coding tools such as Claude Code and Codex in real workflows.
• Engineering discipline: version control, tests, reproducibility.

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 loops stall, tool misuse, or behaviour drift; design guardrails for real traffic
• Measure agent quality continuously with evaluation sets, regression checks, and analysis that explains failures
• Tune tradeoffs that affect latency, answer quality, and token cost in production
• Integrate agents with the systems and data they depend on, moving from prototype to production-ready implementation

Skills

Agent frameworks

Production Python

Claude Code / Codex

System reliability

Tools

Claude Agent SDK

deepagents

LangGraph

Kafka

Job description

As a Senior AI Engineer on agentic systems you'll 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

Qualifications

What You'll 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 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.

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.

Job Info

• Job Identification 35578
• Job Schedule Full time
• Regular or Temporary Regular
• Job Category Analytics Engineering
• Business Unit Canada
• Legal Employer Chubb Insurance Company of Canada