Senior Backend Engineer

Senior Backend Engineer, AI Platform

Chubb's AI Platform team is building an enterprise AI platform powering RAG-based search, document intelligence, and AI-assisted workflows for thousands of users across the global organization. The backend spans three production services: a TypeScript NestJS middleware layer handling authentication, data access, and business logic; a Python FastAPI RAG orchestration service integrating Azure AI Search and Azure OpenAI; and a Python FastAPI document processing pipeline parsing, chunking, and embedding enterprise documents at scale.

The Senior Backend Engineer, AI Platform owns the backend surface. You are the primary engineer on the NestJS middleware layer and a meaningful contributor to the Python services. You ship production-grade code sprint over sprint, operate within the Crucible SDLC framework, and hold the quality bar without needing to be reminded. This role is hands-on by design — senior here means depth of craft and proximity to delivery, not distance from it.

Key Responsibilities

NestJS Middleware — Primary Ownership

• Design and implement feature modules end-to-end: DTOs, controllers, services, and providers following established module patterns across domain areas including authentication, user management, AI integration, file handling, and conversation threading
• Enforce JWT authentication guards, integrate Azure Cosmos DB using parameterized queries (never interpolated), and instrument all contributions with OpenTelemetry distributed tracing
• Author and maintain Swagger/OpenAPI documentation for every new and modified endpoint — API contracts are first-class deliverables
• Apply structured OgmaLogger logging consistently across all contributions; structured observability is a non-negotiable part of every feature
• Write unit tests and e2e tests as part of delivery — mock Cosmos DB and external dependencies, maintain meaningful coverage without being directed to do so

Python FastAPI — Active Contributor

• Contribute production-ready route handlers, Pydantic models, and async service logic to the RAG orchestration service and the doc-parser-api document processing pipeline
• Implement and extend Azure AI Search integrations, embedding pipelines, and document ingestion workflows — batch embedding operations, OCR fallback paths, and multi-format document processors
• Execute quality gates on all Python contributions: ruff linting, mypy static type checking, pytest with a minimum 80% coverage threshold, and bandit security scanning

Crosscutting

• Participate in code reviews across all repositories; provide substantive, reasoned technical feedback and uphold Conventional Commits and Semantic Versioning standards
• Leverage agentic coding tools — Claude Code, GitHub Copilot, and their successors — as daily productivity multipliers within a disciplined engineering workflow
• Collaborate with AI/ML engineers, frontend engineers, and product managers to translate complex AI capabilities into reliable, observable, production-ready backend services
• Contribute to backend architectural decisions: module design, API contract conventions, Azure service integration patterns, CI/CD pipeline configuration, and cross-service dependency management

Qualifications

Required Qualifications

• 5–7 years of professional backend engineering experience delivering production services at meaningful scale
• TypeScript — rigorous typing discipline in a Node.js backend context; you think in types and leverage the type of system rather than work around it
• NestJS or equivalent Node.js backend framework (Express, Fastify) — experience designing feature modules, middleware, guards, interceptors, and RESTful API architecture
• Python — comfortable authoring production-grade services with Pydantic data models and async/await patterns; you write clean Python and know when something is good enough versus when it needs to be better
• Azure ecosystem — practical experience with managed identity (DefaultAzureCredential), Key Vault, Cosmos DB, Blob Storage, and Application Insights; secrets are never hardcoded
• Testing discipline — writes unit, integration, and e2e tests as an intrinsic part of delivery, not a post-hoc checkbox; knows the difference between a test that proves correctness and one that just passes
• Observability fundamentals — structured logging, distributed tracing, and error tracking in a cloud-hosted service; you know what to instrument and why
• Git workflow fluency — feature branching, pull request workflows, code review participation, and CI/CD integration; you leave the codebase in a better state than you found it
• Proven delivery track record — demonstrable history of shipping features into production environments on schedule, sprint over sprint

Nice To Have

• Azure AI Search, Azure OpenAI, or RAG architecture — hands-on experience with vector search, embedding pipelines, or LLM integrations in production
• OpenTelemetry instrumentation — distributed tracing across polyglot service boundaries, especially Python and Node.js in the same trace
• Agentic coding assistants — Claude Code, GitHub Copilot, or equivalent tools used as a genuine workflow accelerant; you know how to direct an agent, review its output critically, and integrate it into a disciplined engineering process
• Document processing pipelines — OCR, PDF/DOCX parsing, text chunking strategies, or file ingestion workflows at scale
• Regulated industry exposure — insurance, financial services, or healthcare environments where data privacy, auditability, and compliance shape engineering decisions
• Angular or React familiarity — enough to read frontend code, understand API consumption patterns, and collaborate on contract design without handholding from the UI team
• Open-source contributions — anything that demonstrates you care about the craft beyond the job

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 31050
• Job Schedule Full time
• Regular or Temporary Regular
• Job Category Analytics Engineering
• Business Unit Canada
• Legal Employer Chubb Insurance Company of Canada
• Posting Date 03/02/2026, 05:18 PM