Hire a RAG Engineer
Hire a RAG engineer to build retrieval-augmented generation over your own data — chunking, embeddings, pgvector/HNSW, hybrid search, reranking, and evals. Remote, senior.
Want to hire a RAG engineer to make an LLM answer from your data instead of making things up? I’m Kuldeep Pisda — a senior backend engineer and former startup CTO in Bengaluru who has built retrieval and semantic search in production, including Hindi-language semantic search on PostgreSQL with pgvector.
What I build
- Retrieval pipelines — ingestion, chunking that respects document structure, embedding, and the storage/index choice that fits your recall and latency targets.
- pgvector and vector stores — HNSW/IVFFlat indexes on PostgreSQL (so your vectors live next to your data), or a dedicated vector DB when that’s genuinely warranted.
- Hybrid search — combining keyword/full-text with vector similarity, because pure embeddings miss exact matches and pure keyword misses meaning.
- Reranking — a second-stage reranker so the top results are the most relevant, not just the nearest vectors.
- Grounding and citations — answers tied back to source passages, so users (and you) can verify what the model claimed.
- Evals — a retrieval/answer eval set so you can tell whether a change helped, instead of guessing from a handful of demos.
Why PostgreSQL-native RAG is often the right call
If your data already lives in Postgres, pgvector lets retrieval sit beside it — one database to operate, transactional consistency, and no separate system to keep in sync. I’ve built and tuned exactly this, and I’ll tell you when your scale genuinely justifies a dedicated vector store instead.
How engagements work
A focused build of a retrieval feature, a rescue of a RAG prototype that hallucinates or returns junk, or a review of an existing pipeline’s chunking, indexing, and eval strategy. Remote, IST, US/EU overlap.
FAQ
Why does our RAG return irrelevant answers? Usually chunking or retrieval, not the model — bad chunk boundaries, no hybrid search, or no reranking. That’s the first thing I’d look at.
pgvector or a dedicated vector database? Depends on scale and ops appetite. For most teams already on Postgres, pgvector is simpler and plenty fast; I’ll be honest about where it stops being enough.
Can you cut hallucinations? Better retrieval, grounding with citations, and refusing to answer when confidence is low all help. It’s an engineering problem, not a prompt trick.
How do we know it’s improving? Evals — I’ll set up a retrieval/answer eval set so changes are measured, not vibes.
Do you also do agents and general LLM work? Yes — see hire an LLM engineer.
Let’s talk
Tell me what you want the model to answer from — get in touch, or grab a time below:
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