Principal GenAI Engineer - Knowledge Graph & Graph RAG

Principal GenAI Engineer Knowledge Graph & Graph RAG

Location: Bengaluru (Hybrid 3 Days Work from Office)

Employment Type: Full-Time

Experience: 8 14 Years

Job Summary We are seeking an experienced

Principal GenAI Engineer to lead the design, development, and deployment of enterprise-grade

Generative AI solutions for Fortune 500 clients. The ideal candidate will have deep expertise in

Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Knowledge Graphs, and Graph-RAG architectures , enabling the delivery of scalable, explainable, and production-ready AI applications. This role requires strong technical leadership, hands-on development, and collaboration with cross-functional teams to build next-generation AI solutions.

Key Responsibilities

• Design and implement enterprise-scale Generative AI solutions using Large Language Models (LLMs).
• Architect and develop Graph-RAG systems by combining Knowledge Graphs with vector search for accurate, context-aware AI responses.
• Build intelligent AI agents using LangChain, LangGraph, and modern orchestration frameworks.
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge discovery.
• Design, implement, and maintain scalable Knowledge Graph architectures, semantic models, ontologies, and taxonomies.
• Implement entity resolution, relationship extraction, graph enrichment, and semantic reasoning.
• Integrate Knowledge Graphs with vector databases to improve AI explainability and reduce hallucinations.
• Deploy and manage AI workloads on AWS, Azure, or Google Cloud Platform (GCP).
• Collaborate with data scientists, software engineers, and business stakeholders to deliver production-ready AI solutions.
• Mentor engineering teams and establish best practices for GenAI development and deployment.

Required Skills

• 8 14 years of experience in Software Engineering, Machine Learning, or Artificial Intelligence.
• 2+ years of hands-on experience developing applications using Large Language Models (LLMs).
• Strong expertise in: + Python + LangChain + LangGraph + SQL
• Experience with: + Retrieval-Augmented Generation (RAG) + AI Agents + Prompt Engineering + Vector Databases
• Experience deploying AI applications on AWS, Azure, or GCP.
• Strong understanding of enterprise AI architecture, scalability, and performance optimization.

Preferred Qualifications Knowledge Graph Expertise

• Experience designing and scaling enterprise Knowledge Graph solutions.
• Strong understanding of: + Ontologies + Taxonomies + Semantic Data Models + Entity Resolution + Relationship Extraction + Graph Enrichment
• Hands-on experience with graph databases such as: + Neo4j + Amazon Neptune + TigerGraph + Nebula Graph
• Proficiency with graph query languages such as Cypher or equivalent.
• Experience building hybrid retrieval architectures combining Knowledge Graphs and Vector Databases.
• Ability to integrate structured graph reasoning with LLMs to improve explainability and minimize hallucinations.

Nice to Have

• Experience with Azure AI Foundry, Amazon Bedrock, Google Vertex AI, or OpenAI APIs.
• Familiarity with embedding models, reranking techniques, and semantic search.
• Experience with MLOps, Docker, Kubernetes, and CI/CD pipelines.
• Knowledge of Graph Data Science (GDS), Graph Analytics, and Explainable AI (XAI).

Education Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.