{"id":151126,"url":"https://alion.io/job/chubb-avp-lead-ai-engineer","title":"AVP, Lead AI Engineer","company":{"id":18935,"name":"Chubb","domain":"chubb.com","url":"https://alion.io/company/chubb","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":85,"open_postings":98,"ghost_share":0,"stale_share":0.816,"repost_share":0,"time_to_fill_p50_days":8,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":91000,"max_usd":183000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":8},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hugging Face","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Post-training","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Transformers","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-07-21T00:00:00Z","employer_posted_date":"2026-07-21","last_verified_at":"2026-10-18T04:28:19Z","board_verified":true,"closed_at":null,"days_open":89,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":89},"description":"OVERVIEW\nDrive AI engineering workstreams across the Audit+ program, ensuring compliance of UW and Claims processing, automation of controls, as well as driving cost reduction.\nROLE\nThis 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.\nFOCUS AREAS & RESPONSIBILITIES\nDevelop the next generation of AI driven Audit+ platforms and AI assets, including Agentic framework\nBuild scalable pipelines for data ingestion, feature engineering, model training, evaluation, and monitoring\nDevelop and integrate generative AI applications, including LLM-based workflows, agents, and retrieval-augmented generation (RAG) solutions\nEnsure solutions meet security, privacy, compliance, and responsible AI standards\nOptimize model performance, reliability, latency, and cost across the AI lifecycle\nPlatform 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\nEnable sales analytics | marketing with a foundational layer of AI with Consumer LLM as needed\nDrive implementation and change management in collaboration with regional D&A leads, business and technology partners\nWork with the Consumer+ Platform Engineering team to develop reusable Foundational AI Assets | Applications to accelerate local deployments\nSupport Business Development by evangelizing our AI success stories to stakeholders and sponsors as needed\nEnable the regional and local teams to leverage Global Consumer+ platforms and be self-sufficient\nOperating Network:\nWork closely with regional Data & Analytics teams to identify opportunities and assist in implementation\nCollaborate with Regional IT, GDO, Global Analytics, Ops for data | infra | integration related to implementation\nCollaborate with teams to enforce responsible AI, model risk management, and AI governance\nCandidate Profile:\nTechnical Skills\nStrong hands-on coding ability in Python plus at least one additional language (TypeScript, Go, or Java); disciplined about clean code, design docs, and code review.\nDeep 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.\nExperience with inference optimization and high-throughput serving frameworks.\nProven experience shipping and operating high-scale services on Docker/Kubernetes with CI/CD pipelines (GitHub Actions, Jenkins, or similar).\nExperience with event-stream/service-integration technologies (e.g., Kafka) and building resilient, observable production systems (SLOs for latency, error rate, availability).\nExperience building end-to-end ML pipelines: data ingestion, feature engineering, model training, evaluation, and monitoring.\nExperience integrating AI/LLM services into user-facing products (APIs, SDKs, real-time UX features).\nGovernance, Risk & Compliance\nWorking knowledge of responsible AI practices, model risk management, and AI governance frameworks.\nExperience Ensuring AI solutions meet security, privacy, and regulatory compliance standards, particularly in audit, underwriting, or claims-adjacent contexts.\nLeadership & Collaboration\nAbility to architect and own robust, scalable engineering solutions while remaining hands-on with code.\nExperience partnering cross-functionally with regional Data & Analytics teams, IT, GDO/Ops, front-end, and DevOps stakeholders to drive implementation and change management.\nAbility to represent technical architecture, trade-offs, and AI risk to both engineering leaders and non-technical executives with clarity and confidence.\nAttributes\nOutstanding written and verbal communication across technical and executive audiences.\nBias for action and comfort making high-impact decisions under uncertainty.\nAbility to drive KPI/OKR-based delivery in an iterative, sprint-based environment.\nChubb Canada does not use artificial intelligence (AI) tools to assess, screen, or select applicants.\nAt 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. 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