Job Description & Details
This is a hands-on backend and platform engineering role focused squarely on shipping production-ready Generative AI capabilities rather than theoretical research. You will be building domain-aligned AI solutions like RAG-based search, chatbots, and copilot services that plug directly into real enterprise workflows.
What You'll Actually Be Doing
Your day-to-day will involve designing, building, and deploying robust backend services and cloud infrastructure that power enterprise AI applications such as benefits, claims, and membership chatbots. You'll spend a lot of time writing TypeScript, integrating AWS services via CDK, and managing the end-to-end lifecycle of APIs and platform components. The work requires balancing high-output feature delivery with strict adherence to security, privacy, and observability standards.
The Core Tech Stack
Node.js and TypeScript are absolute non-negotiables here for your primary backend language. On the infrastructure side, deep hands-on AWS experience is critical—specifically services like Lambda, API Gateway, DynamoDB, PostgreSQL, and critically, AWS Bedrock. You must also be comfortable implementing Infrastructure as Code using AWS CDK, along with understanding core RAG concepts like embeddings, chunking, semantic retrieval, and basic safety guardrails against hallucinations.
Interview Expectations
Expect the engineering panel to drill down into your architectural decisions around RAG pipelines, asking you to explain how you handle chunking strategies and semantic retrieval latency at scale. They will also likely test your practical knowledge of AWS CDK and serverless patterns in Node.js, probing to see if you can articulate how to secure enterprise APIs and manage state safely within distributed cloud environments.
Application Advice
When tailoring your resume for this contract role, make sure Node.js, TypeScript, AWS CDK, and AWS Bedrock are prominently featured near the top since these are the strict gatekeeper keywords for the ATS. Explicitly highlight any production experience you have building RAG applications, vector search implementations, or enterprise chatbots rather than generic full-stack web projects.