Applied AI Sr Associate Engineer

• 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.

Responsibilities

• Build an end-to-end AI-assisted operations platform that replaces manual Salesforce workflows and spreadsheet-based processes with an auditable, intelligent console.Implement the full AI-driven platform architecture across multiple production phases; build the React 18 + TypeScript portal spanning all stakeholder views; engineer the FastAPI Python workflow service with async Excel-in/Excel-out processing, Celery task queuing, and status polling.
• Provision and maintain the Salesforce Connected App; manage the OAuth token lifecycle; implement read operations across Cases, Accounts, Contacts, Tasks, and Activity History; build validated Salesforce write-back flows (merge, email update, segment patch) with AI pre-flight checks and analyst confirmation gates before any SFDC commit; implement real-time case state monitoring via Salesforce Platform Events to drive escalation detection.
• Integrate case summarization, duplicate analysis, action pre-flight checks, and an AI chat assistant pre-seeded with live case context; implement the escalation prediction engine; enforce human-in-the-loop guardrails at the API layer.
• Manage phased delivery with defined go-live milestones; lead UAT sessions with analysts, managers, and leadership at each phase go-live, and incorporate feedback into the subsequent sprint backlog; maintain Azure infrastructure, monitor application health, and own incident response throughout the build period.
  • Document architecture decisions, API contracts, data schemas, and operational runbooks to support long-term maintainability beyond the build phase.