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Ask HN: Who wants to be hired? (October 2026)

129 points · 518 comments · whoishiring

  1. naman_411 · · focus · HN ↗
    Naman Gupta | India | Remote only | Contract (B2B, USD/EUR) or full-time

    AI engineer focused on retrieval quality and agent reliability. I measure retrieval before changing it.

    - Built and evaluated a production document-AI retrieval pipeline and LangGraph workflows.

    - rag-eval-harness (open source): 20 CUAD contracts, 50 queries, 114 annotated spans, 7 retrieval configs. Clause-aware chunking kept 99.1% of answer spans intact vs 43.0% for fixed-width. Hybrid BM25 + dense with RRF scored best (MRR 0.70). The reranker did not help. 87 unit tests.

    - Merged PR in Lamatic/AgentKit: EvidenceFit, compares chunking strategies against labelled evidence spans and returns a SHIP/TUNE/BLOCK verdict.

    - MCPHub: MCP server testing and inspection across SSE, Streamable HTTP and stdio, with latency percentiles.

    For contracts: a 5-day fixed-scope retrieval baseline on your pipeline (labelled eval set, failure patterns, prioritized fixes, harness handed over).

    Stack: Python, TypeScript, LangGraph, FastAPI, Next.js, Qdrant, pgvector, Postgres, Docker.

    Proof: <a href="https:&#x2F;&#x2F;namangupta.dev&#x2F;production-ai" rel="nofollow">https:&#x2F;&#x2F;namangupta.dev&#x2F;production-ai

    GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;namanxdev&#x2F;rag-eval-harness" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;namanxdev&#x2F;rag-eval-harness

    Email: naman@namangupta.dev

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