Full-Stack/LLM Development

I’m replacing our summary-based workflow with a true Retrieval-Augmented Generation pipeline and need a collaborator who can own both the backend and the React front end.

The core objective is accuracy: every PDF we receive from clients must be chunked, embedded, stored in Qdrant, and then retrieved at query time so the language model can cite exact passages, never a lossy summary. You’ll wire this flow together with Python services and Node/Fastify APIs, expose it to the UI in Typescript/React, and keep everything humming inside our OpenAI-powered evaluation layer.

What I’ll lean on you for
• Architecting and implementing the end-to-end RAG pipeline (chunking strategy, embedding jobs, vector-store schema, retrieval functions).
• Building real-time document retrieval endpoints that push grounded evidence straight into assessments, role-plays, and feedback modules.
• Instrumenting detailed logging and audit trails so compliance teams can trace every answer back to source text.
• Crafting a clean, responsive React interface for document upload, status monitoring, and citation-rich results.

Stack you’ll touch: Python, Node, Fastify, Typescript, React, Qdrant, OpenAI, plus whatever lightweight ops you prefer for deployment.

Logistics
We’ll start part-time with daily overlap in GMT+8; if we click, there’s plenty of runway to extend the engagement. I’m hands-on and will be building alongside you, so expect tight feedback loops and a true collaboration.
Must be available to work 4 hours per day
Must be available as soon as possible once you hired
If making embeddings trustworthy and auditable gets you excited, let’s talk.