Graph & GenAI Engineer

Senior / Lead Knowledge Graph & GenAI Engineer

Role Summary

We are seeking a Senior / Lead Knowledge Graph & GenAI Engineer to design, build, and scale largescale knowledge graph platforms and Graph RAG systems for reasoningheavy GenAI applications. This role requires strong expertise in Neo4j, Cypher, graph data modeling, and Graphbased RAG frameworks (LangChain, LangGraph), along with handson experience managing terabytescale graph databases in production.

You will work at the intersection of graph databases, LLMs, and distributed systems, enabling highimpact, AIdriven knowledge platforms.

Key Responsibilities

• Design, model, and optimize enterprisescale Knowledge Graphs for complex and evolving domains.
• Define graph schemas, ontologies, taxonomies, and relationship strategies for highcardinality data.
• Build, operate, and optimize Neo4j clusters (Aura, Selfmanaged, or Enterprise).
• Create, update, and manipulate graph data at scale using advanced Cypher queries.
• Design scalable batch and streaming ingestion pipelines for incremental graph updates.
• Manage and optimize TBscale graph datasets, including performance tuning, backups, replication, and high availability.
• Work with large or distributed graph systems (e.g., InfiniGraph or similar enterprise platforms) and address challenges in storage, traversal latency, and memory optimization.
• Build Graph RAG pipelines combining Neo4j with LLMs for grounded, explainable responses.
• Implement LangChain graph integrations, custom retrievers, and graphaware tools.
• Design LangGraph workflows for multistep reasoning, stateful execution, and agent orchestration.
• Combine Graph RAG and Vector RAG for hybrid retrieval strategies.
• Develop LLMpowered applications using OpenAI, Azure OpenAI, or opensource models, including prompt engineering and tool/function calling.
• Define endtoend GenAI + Graph architectures (ingestion graph retrieval inference).
• Build APIs and microservices, ensure security and access control, and collaborate with data, ML, and product teams.

Required Skills & Experience

• Strong handson experience with Neo4j (Enterprise preferred).
• Advanced proficiency in Cypher, including indexing, constraints, and query optimization.
• Proven experience managing large, complex, and evolving graph datasets.
• Experience operating TBscale databases with performance and reliability constraints.
• Experience with distributed or enterprise graph databases (e.g., InfiniGraph or similar).
• Solid understanding of graph theory, traversal patterns, and graph algorithms.
• Handson experience with LangChain and LangGraph.
• Strong understanding of RAG architectures, especially Graph RAG.
• Experience integrating LLMs with structured data sources; familiarity with embeddings and vector databases.
• Strong Python development skills.
• Experience building REST / GraphQL APIs.
• Familiarity with cloud platforms (Azure, AWS, or GCP) and Docker, Kubernetes, CI/CD.