AI / RAG / Applied ML Engineer

Role Overview

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.

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.
• Strong understanding of embeddings and vector search.
• Strong Python and SQL skills.
• Experience designing evaluations and benchmarks for LLM correctness and safety.
• Strong 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

Skills

• Python
• SQL
• LLM
• RAG
• Vector Search