Has shipped at least one production LLM agent (tool-using, not a chat wrapper). They should be able to describe what broke and how they measured quality.
Hands-on with Claude SDK / OpenAI Assistants / LangGraph (Claude SDK preferred — your stack)
RAG in production: chunking strategy choices, hybrid search, retrieval evaluation
Vector DB experience: Qdrant, pgvector, or Pinecone
Treats prompt engineering as an engineering discipline — has eval suites, not just "vibes-based" iteration
Has built at least one system with feedback loops (not static RAG that's frozen on day one)
Solid nice-to-haves
Frappe / ERPNext experience (willingness to learn it is fine — it's quirky but documented)
Insurance, fintech, or another regulated-data domain
Telegram Bot API
Document AI: Google Vision, Textract, or layout-aware OCR
Eval frameworks: Braintrust, LangSmith, or has rolled their own
Skills:- Python, Large Language Models (LLM), OpenAI API and LangGraph
📌 Senior AI Engineer â Agentic Systems (Sahibzada Ajit Singh Nagar)
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📍 Sahibzada Ajit Singh Nagar
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