Principal Gen AI Engineer - 64654
Turing
About the role
Location: Onsite at Bengaluru- 3 Days WFO
Employment Type: Full Time
Experience Level: Principal
About the Role:
Turing is hiring a Principal GenAI Engineer with strong expertise in LLMs and Knowledge Graphs to lead enterprise-scale AI implementations for Fortune 500 clients. 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’re Looking For: • 10+ years of experience in ML/AI systems • 2+ years hands-on experience with LLMs (RAG, agents, prompt engineering) • 5+ years of production experience working with Knowledge Graphs • Strong proficiency in Python, LangChain, LangGraph, and SQL • Experience deploying GenAI systems on AWS / Azure / GCP
Mandatory 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
Requirements
- 10+ years of experience in ML/AI systems
- 2+ years hands-on experience with LLMs (RAG, agents, prompt engineering)
- 5+ years of production experience working with Knowledge Graphs
- Strong proficiency in Python, LangChain, LangGraph, and SQL
- Experience deploying GenAI systems on AWS / Azure / GCP
- Experience with Neo4j, Amazon Neptune, or similar graph databases
- Strong hands-on experience with Cypher (or similar graph query languages)
Responsibilities
- Lead enterprise-scale AI implementations for Fortune 500 clients
- Build Graph-powered RAG systems (Graph-RAG)
- Combine structured semantic reasoning with advanced LLM architectures
- Design and scale enterprise Knowledge Graph architectures
- Develop ontologies, taxonomies, and semantic data models
- Implement entity resolution, relationship extraction, and graph enrichment
- Build hybrid retrieval systems combining Knowledge Graphs and vector databases
- Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability
Skills
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