Full Stack AI Developer needed to fix and improve an existing RAG - based pipeline

We are looking for an experienced full-stack AI developer proficient in RAG and LLM integration for a short-term task. You will be responsible for auditing, debugging, and improving the retrieval accuracy of our current implementation. We are specifically seeking someone who has shipped real production RAG systems and successfully resolved latency and context precision issues. You must have strong expertise with VectorDBs (Pinecone, ChromaDB), embedding-based retrieval, hybrid search, re-ranking, and advanced prompt engineering. Our current setup uses Python, OpenAI's API, and a vector database - the issues are mainly around context relevance, retrieval precision, and inconsistent or hallucinated answers when processing documents with highly similar content. To apply, please share a concrete example of a production RAG system you have developed, along with your immediate availability to start this week.