Job Description :

This role focuses on building Graph-powered RAG systems (Graph-RAG) that combine structured semantic reasoning with advanced LLM architectures to deliver scalable, explainable, production-grade AI solutions.

What We Are Looking For :

- 8-13 years of experience in ML/AI systems

- 2+ years hands-on experience with LLMs (RAG, agents, prompt engineering)

- Strong proficiency in Python, LangChain, LangGraph, and SQL

- Experience deploying GenAI systems on AWS / Azure / GCP

Knowledge Graph Expertise :

- Design and scale enterprise Knowledge Graph architectures

- Develop ontologies, taxonomies, and semantic data models

- Implement entity resolution, relationship extraction, and graph enrichment

- Experience with Neo4j, Amazon Neptune, or similar graph databases

- Strong hands-on experience with Cypher (or similar graph query languages)

- Build hybrid retrieval systems combining Knowledge Graphs + vector databases

- Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability