Avp, Lead Ai Engineer

OVERVIEWDrive AI engineering workstreams across the Audit+ program, ensuring compliance of UW and Claims processing, automation of controls, as well as driving cost reduction.
ROLEThis role will work closely with regional teams to identify and build production grade foundational capabilities, platform those capabilities to enable rapid operationalization and scale out of Audit+ AI capabilities. The primary focus is on end‑to‑end automation of controls across Claims, UW, Finance and Technology.
FOCUS AREAS & RESPONSIBILITIES
Develop the next generation of AI driven Audit+ platforms and AI assets, including Agentic framework
Build scalable pipelines for data ingestion, feature engineering, model training, evaluation, and monitoring
Develop and integrate generative AI applications, including LLM-based workflows, agents, and retrieval‑augmented generation (RAG) solutions
Ensure solutions meet security, privacy, compliance, and responsible AI standards
Optimize model performance, reliability, latency, and cost across the AI lifecycle
Platform capabilities for extending AI enablement to human‑lead operations in areas like QA, Training | Operations for faster and efficient production and introduce efficiencies in distribution workflows
Enable sales analytics | marketing with a foundational layer of AI with Consumer LLM as needed
Drive implementation and change management in collaboration with regional D&A leads, business and technology partners
Work with the Consumer+ Platform Engineering team to develop reusable Foundational AI Assets | Applications to accelerate local deployments
Support Business Development by evangelizing our AI success stories to stakeholders and sponsors as needed
Enable the regional and local teams to leverage Global Consumer+ platforms and be self‑sufficient
Operating Network:
Work closely with regional Data & Analytics teams to identify opportunities and assist in implementation
Collaborate with Regional IT, GDO, Global Analytics, Ops for data | infra | integration related to implementation
Collaborate with teams to enforce responsible AI, model risk management, and AI governance
QualificationsCandidate Profile:Technical Skills
Strong hands‑on coding ability in Python plus at least one additional language (TypeScript, Go, or Java);
disciplined about cleancode, design docs, and code review.
Deep knowledge of modern LLM tooling and techniques: Hugging Face Transformers, prompt engineering, post‑training/fine‑tuning pipelines, retrieval‑augmented generation (RAG), and agentic AI frameworks.
Experience with inference optimization and high‑throughput serving frameworks.
Proven experience shipping and operating high‑scale services on Docker/Kubernetes with CI/CD pipelines (GitHub Actions, Jenkins, or similar).
Experience with event-stream/service‑integration technologies (e.G., Kafka) and building resilient, observable production systems (SLOs for latency, error rate, availability).
Experience building end‑to‑end ML pipelines: data ingestion, feature engineering, model training, evaluation, and monitoring.
Experience integrating AI/LLM services into user‑facing products (APIs, SDKs, real‑time UX features).
Governance, Risk & Compliance
Working knowledge of responsible AI practices, model risk management, and AI governance frameworks.
Experience Ensuring AI solutions meet security, privacy, and regulatory compliance standards, particularly in audit, underwriting, or claims‑adjacent contexts.
Leadership & Collaboration
Ability to architect and own robust, scalable engineering solutions while remaining hands‑on with code.
Experience partnering cross‑functionally with regional Data & Analytics teams, IT, GDO/Ops, front‑end, and DevOps stakeholders to drive implementation and change management.
Ability to represent technical architecture, trade‑offs, and AI risk to both engineering leaders and non‑technical executives with clarity and confidence.
Attributes
Outstanding written and verbal communication across technical and executive audiences.
Bias for action and comfort making high‑impact decisions under uncertainty.
Ability to drive KPI/OKR‑based delivery in an iterative, sprint‑based environment.
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 35019
Job Schedule Full time
Regular or Temporary Regular
Job Category Analytics Engineering
Business Unit Canada
Legal Employer Chubb Insurance Company of Canada
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