Senior AI developer

Aon Corporation•Toronto, ON

11h•Hybrid

About The Position

Are you excited about using Generative AI to solve real‑world insurance and actuarial challenges? Do you want your work to influence how global insurers make smarter, faster decisions? As a Senior AI Developer within Aon Life Solutions, part of Aon’s Reinsurance Strategy and Technology Group, you’ll help deliver innovative GenAI solutions that drive measurable impact for clients worldwide. If you’re looking to work at the intersection of AI, analytics, and life reinsurance—while growing in a collaborative, high‑impact environment—could this be your next move? The salary range for this position is CAD $128,300 - CAD $171,100}. The actual salary will vary based on applicant’s education, experience, skills and abilities, as well as internal equity. The salary may also be adjusted based on applicant’s geographic location. Aon also offers you a generous incentive earning opportunity and a comprehensive benefits package. Aon is in the business of better decisions At Aon, we shape decisions for the better to protect and enrich the lives of people around the world. As an organization, we are united through trust as one inclusive team and we are passionate about helping our colleagues and clients succeed.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field (or equivalent practical experience).
7+ years of experience in Python software engineering, including 2–3 years of demonstrable experience building LLM / GenAI / agentic systems using modern Python frameworks.
Hands‑on experience designing and implementing RAG systems, including document ingestion and preprocessing, chunking and embedding strategies, retrieval and re‑ranking approaches, and prompt construction.
Proven experience building tool‑using agents (e.g., orchestrating external tools/APIs, validation steps, and reasoning chains).
Demonstrated capability in prompt engineering and prompt optimization (e.g., system prompts, few‑shot examples, evaluation and refinement).
Experience defining, implementing, and using evaluation frameworks and metrics for LLM/GenAI systems (e.g., offline benchmarks, success criteria for specific workflows, human‑in‑the‑loop evaluation or A/B tests).
Familiarity with vector databases, orchestration frameworks, and MLOps / LLMOps tooling.
Strong understanding of Agile methodologies and the software development life cycle (SDLC).
Experience with collaboration and delivery tools such as Azure DevOps, Jira, and Confluence.

Nice To Haves

Prior experience in actuarial, risk, or financial modeling domains is strongly preferred; alternatively, a proven ability to quickly learn and work with complex financial or quantitative systems is essential.

Responsibilities

Identify, design, and prototype GenAI agents to support PathWise model design, build, testing, and documentation activities.
Design and implement RAG‑based solutions (document ingestion, chunking, embeddings, retrieval strategies, re‑ranking, prompt construction) to make PathWise knowledge and model documentation easily discoverable and actionable.
Develop and iterate on prompts, tools, and workflows within prototypes so that they can be properly evaluated, refined, and specified for production implementation.
Define and track evaluation metrics (e.g., answer quality, latency, robustness, safety) for GenAI agents, and use these to guide iteration and recommendations to the core development team.
Run structured experiments, gather feedback from actuaries and other users, and translate findings into clear technical specifications for production teams.
Implement and maintain appropriate controls, guardrails, and governance to ensure compliance with Aon’s AI requirements, model governance standards, and relevant regulatory expectations.
Embed observability, monitoring, and evaluation into GenAI agents to improve reliability, transparency, and performance.
Champion Aon’s AI governance requirements, including safety, responsible use of data, and clear documentation of model and agent behavior.
Collaborate closely with Actuaries and PathWise users to understand their workflows, gather requirements, and co‑design solutions.
Collaborate closely with Product management to prioritize features, define success metrics, and shape the AI agent roadmap.
Collaborate closely with PathWise Development & Infrastructure teams to prepare prototypes for hardened implementation, including deployment, security, and monitoring considerations.
Provide technical leadership by identifying new and creative ways to apply GenAI and automation to actuarial and financial modeling.
Provide technical leadership by mentoring colleagues and sharing knowledge on GenAI, RAG, and agentic system design.