Applied AI Sr Associate Engineer

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence. If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. Skills and Competencies 3–5 years of hands-on full-stack engineering experience with a track record of independently delivering production-quality applications end to end; proficiency in React 18 with TypeScript (component architecture, hooks, state management with Zustand or Redux, and React Router for complex multi-view portals), Node.js/Express for BFF APIs (including authentication flows, request proxying, caching, and structured error handling), and Python/FastAPI for backend microservices and data processing pipelines. Solid understanding of SQL, PostgreSQL (schema design, query optimization, indexing strategies, and workflow state persistence) and Redis (caching, session management, and task queue brokering via Celery or equivalent); proficiency with Docker for containerizing Node.js and Python services with identical dev/prod environments; ability to build and maintain GitHub Actions CI/CD pipelines covering lint, unit test, build, and zero-downtime deploy on merge. Experience integrating with Salesforce REST APIs and SOQL — reading Cases, Accounts, Contacts, Tasks, and Activity History at volume — is preferred; familiarity with Salesforce Connected App setup, OAuth 2.0 flows, and per-user token management; experience triggering write-backs via Flows or Apex REST endpoints with validation gates and audit logging; exposure to Salesforce Platform Events or Pub/Sub API for real-time case state change handling is a strong plus. Hands-on experience integrating LLM models into production applications — prompt engineering, context management, structured output parsing, and cost control; familiarity with LangChain (Python) or equivalent orchestration frameworks for chaining LLM calls, managing memory, and enforcing guardrails; understanding of embedding-based semantic search (text-embedding-ada-002 or similar) for similarity scoring and duplicate detection; commitment to human-in-the-loop design — AI assists and drafts; humans review and commit — enforced at the API layer, not just the UI. Proficiency with Pandas and openpyxl for Excel-in/Excel-out data processing pipelines; experience with fuzzy matching libraries (RapidFuzz or similar) for entity deduplication and overlap analysis; exposure to graph traversal (NetworkX or equivalent) for ownership hierarchy and relation analysis; ability to integrate with external data APIs. Strong grasp of REST API design principles including versioning, error contracts, pagination, and backward compatibility; ability to write well-structured, tested code — unit tests, integration tests, and API contract tests as part of the development workflow, not an afterthought; experience maintaining an append-only audit log tied to business entity IDs for compliance and traceability; comfortable self-managing across frontend, backend, AI, and infrastructure layers in a high-autonomy environment within a 2-week sprint cadence. Education Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline.