KEY OBJECTIVES:

Chubb is seeking an exceptional AVP, Lead AI Engineer with a passion for hands-on technical leadership and large-scale delivery of production Large Language Model (LLM) systems. This role is built for leaders who thrive on architecting, building, and deploying high-impact AI solutions that power enterprise transformation. We’re looking for an AVP, Lead AI Engineer who is still hands-on with code, obsessed with end-to-end performance, and eager to unify multiple products around a single, enterprise-grade LLM core.

As an AVP, Lead AI Engineer, you will:

• Scale program impact: Design headless service layers that exposes a common set of LLM capabilities to change core insurance processes.

• Code & own: Spend significant time writing and reviewing code while leading 4-8 outstanding engineers. Set engineering standards, architect robust solutions, own codebases, and performant services.

• Full-stack impact: Your APIs, SDKs, and LLM inferences will drive real-time UX features seen by thousands of Chubb users daily.

• Modern stack, real constraints: Leverage the latest in prompt engineering, post-training, and inference acceleration while meeting latency, quality, and uptime SLAs.

• Executive support, global scale: You’ll ship quickly with clear sponsorship, abundant compute, and a mandate to make insurance smarter worldwide.

MAJOR RESPONSIBILITIES

Hands-On Engineering & Delivery

• Write and review production-grade Python plus Docker and Kubernetes for deployments. Build resilient event-stream integrations using Kafka for service communication.

• Employ advanced LLM deployment frameworks (vLLM, Triton, or DeepSpeed-Inference) to optimize serving latency, throughput, and cost efficiency.

• Instrument your services end to end and enforce SLOs for latency, error rate, and availability.

• Ship iteratively every sprint, own engineering planning and delivery, and track impact via clear KPIs and OKRs.

Leadership & Collaboration

• Coach team members on design reviews, code quality, and engineering excellence; cultivate a culture rooted in ownership and continuous improvement.

• Work closely with Engineering stakeholders and team for front-end and DevOps to ensure seamless hand-offs from model output to user interface.

• Represent LLM system architecture and risk trade-offs to engineering leaders, senior executives, and non-technical stakeholders with clarity and confidence.

QUALIFICATIONS

Required Qualifications

• 8-12 years in software/ML engineering, including 3+ years as a tech lead or engineering manager delivering production systems. Prior success deploying ML/AI at scale strongly preferred.

• Strong coding skills in Python and one other language (TypeScript, Go, or Java); fanatic about clean code and design docs.

• Deep knowledge of modern LLM tooling: Hugging Face Transformers, prompt engineering, post-training pipelines, inference optimization (Triton, DeepSpeed-Inference, vLLM).

• Proven track record shipping high-scale services on Kubernetes/Docker with CI/CD (GitHub Actions, Jenkins, or similar).

• Experience integrating AI services into user-facing products.

• Outstanding written and verbal communication. You can debate inference architecture at 9 a.m., then brief execs on model risk at noon.

• Bias for action. You are comfortable making high-impact decisions under uncertainty.

• Track record hiring, mentoring, and retaining high-performing ML, AI, or data engineers.

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.