Live opening · Posted 12 days ago
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About the role
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RAG Engineer
Sweat chunking, retrieval quality, and citations—not just “we added a vector DB.”
Through Nebulai’s marketplace, you embed with client teams building retrieval-augmented systems that answer from their docs and systems of record: ingest pipelines, hybrid search, reranking, grounding checks, and the eval loop that proves it’s getting better. Practical delivery. Accountable quality. Escape the pilot trap.
Strong fit if you:
- Have shipped production RAG (embeddings, chunking, hybrid search, rerankers)
- Are solid in Python and know at least one vector/search stack
- Measure recall@k, faithfulness, and user-visible failure modes
- Work well with data owners and security on access control for retrieved content
Bonus if you’ve:
- Built multi-corpus or permission-aware retrieval
- Combined RAG with agents/tool calling
- Tuned latency/cost without tanking answer quality
Apply at https://nebulai.app
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