Job Summary

Monoedge is looking for an AI / RAG / Applied ML Engineer to build reliable, grounded, and production-ready AI systems.

The role focuses on RAG, natural-language-to-SQL, retrieval quality, LLM evaluation, and applied machine learning, with a strong emphasis on correctness and preventing hallucinated answers.

Key Responsibilities
Build natural-language-to-SQL systems with validation and self-correction mechanisms.
Improve retrieval quality using vector embeddings, semantic retrieval, graph-based retrieval, and ranking.
Ground AI-generated answers in real data and provide appropriate citations.
Build validation mechanisms to prevent hallucinated values and unsupported claims.
Implement query safety and strict multi-tenant data isolation.
Improve automated analytical findings, including quality, ranking, severity calibration, and business-readable output.
Build evaluation frameworks with correctness and safety gates to prevent regressions.
Optimize LLM cost, latency, prompts,



and model selection.

Requirements
Production experience with LLM / RAG systems, not just prototypes.
Solid understanding of embeddings and vector search.
Robust Python and SQL skills.
Experience designing evaluations and benchmarks for LLM correctness and safety.
Robust verification mindset and ability to validate model-generated outputs before trusting them.

Nice to Have
Local embedding models such as sentence-transformers / BGE-class
pgvector
Agentic or multi-step LLM frameworks such as LangGraph
Graph-based retrieval and knowledge graphs
Statistics or causal-inference background

Technology Stack

Python | SQL | LLM APIs | PostgreSQL / pgvector | sentence-transformers | Vector Search | Evaluation Tooling

Pay: ₹1,000,000.00 - ₹1,800,000.00 per year

Work Location: In person

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