Hire AI talent
Hire a Data & Knowledge Engineer for RAG
A specialist who builds the knowledge layer — the data pipelines, chunking, embeddings, and retrieval evals — that makes your LLM apps accurate.
Start in
7–14 days
Rates
from $95/hr
Regions
US · EU · PH · BR
What a Data & Knowledge Engineer does
- Build ingestion pipelines from your docs, tickets, and databases
- Design chunking and embedding strategies tuned to your content
- Choose and operate the right vector store for your scale
- Build retrieval evals so quality is measurable
- Add reranking, hybrid search, and filters as needed
Outcomes you can expect
- A maintained knowledge layer your LLM apps can rely on
- Retrieval evals wired into CI so quality stays high
- Documented refresh cadence for changing content
When to hire
- Your LLM 'knows' less about your business than it should
- Answers are confidently wrong because retrieval is wrong
- You're outgrowing a one-file vector store prototype
FAQ — Hiring a Data & Knowledge Engineer
Do I need a separate RAG engineer or can an AI engineer do it?
Small RAG setups, an AI engineer can handle. Once your content is large, multi-source, or changing frequently, a dedicated knowledge engineer is what keeps retrieval — and answer quality — from quietly drifting.
Which vector databases do you work with?
Pinecone, Weaviate, Qdrant, pgvector, Vespa, OpenSearch, and managed vector services on AWS, Azure, and GCP. The choice is made during design based on your scale, latency, and ops preferences.
Ready to hire a data & knowledge engineer for rag?
Book a 30-minute launch call. We'll confirm fit, share matched candidates, and get the right person started inside two weeks.