Hiring Guide
RAG backend engineer for production AI systems.
I build RAG backends around ingestion, permissions, retrieval quality, observability, cost controls, and model integration that can survive real users.
Who this is for
This page is for teams moving past a chatbot demo into a production AI backend with documents, permissions, customers, evaluation, and operational risk.
Best fit projects
- Productionizing an internal knowledge assistant or customer-facing AI feature
- Designing ingestion, chunking, embedding, and re-indexing workflows
- Adding permission-aware retrieval and source citations
- Building FastAPI/OpenAI/vector search backend services
Relevant proof
- Backend positioning includes AI/RAG production systems, not only prompt work
- Published a detailed RAG production checklist focused on backend failure modes
- Experience with vector search, Typesense, OpenAI integrations, and production API design
How I work
- Map source systems, permission rules, and answer-quality requirements
- Design async ingestion and observable processing jobs
- Make retrieval permission-aware before model calls happen
- Add evaluation examples, rollback paths, and cost visibility
Related proof pages
Questions founders usually ask
What breaks first in production RAG systems?
Usually ingestion, permissions, evaluation, stale data, and observability. The model call is rarely the whole system.
Can you build the backend around OpenAI or another model provider?
Yes. The backend should keep provider choice flexible while owning ingestion, retrieval, authorization, logging, and answer-quality controls.
Next step
Send the current stack, the bottleneck, and the business outcome you want. I will reply with the smallest useful next step: audit, scoped implementation, or a short technical plan.
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