We are seeking a highly skilled Graph Database Expert to join our cutting-edge agentic AI team, where we're building sophisticated generalised agentic workflows for diverse business applications and building an ecosystem around agentic frameworks which includes Kodosumi, Masumi, and Sokosumi platforms. This role is critical in architecting and implementing knowledge-driven AI systems that power intelligent autonomous agents.
Key Responsibilities
Knowledge Architecture & Design:
Design and implement scalable knowledge graphs and knowledge bases that serve as the foundational intelligence layer for our agentic systems
Architect graph database solutions that can efficiently represent complex business domains, relationships, and dynamic knowledge structures Graph Database Engineering
Build and optimize high-performance graph databases using technologies like Neo4j, Amazon Neptune, ArangoDB, or similar platforms
Implement efficient graph traversal algorithms and query optimization strategies for real-time agentic decision-making
Design graph schemas that support both structured knowledge representation.
GraphRAG & LLM Integration:
Develop and implement GraphRAG (Graph Retrieval-Augmented Generation) systems that enhance LLM capabilities with structured knowledge
Create sophisticated retrieval mechanisms that combine vector embeddings with graph relationships for contextually rich agent responses
Build bridges between traditional graph databases and modern LLM architectures to enable seamless knowledge integration
Agentic Workflow Enhancement:
Design knowledge persistence and retrieval patterns that support multi-agent collaboration and learning
Implement graph-based reasoning capabilities that enable agents to make informed decisions across complex business scenarios
Performance & Scalability:
Optimize graph database performance for high-throughput agentic operations across multiple concurrent workflows
Implement caching strategies and query optimization techniques specific to agent-driven knowledge access patterns
Design distributed graph architectures that can scale with growing knowledge bases and increasing agent populations
Required Qualifications
Technical Expertise:
5 years of hands-on experience with graph databases (Neo4j, Amazon Neptune, ArangoDB, TigerGraph, etc.)
Deep understanding of graph theory, algorithms, and data modelling principles
Proven experience in building and deploying knowledge graphs at enterprise scale
Strong proficiency in graph query languages (Cypher, Gremlin, SPARQL)
AI & Machine Learning:
Solid understanding of Large Language Models (LLMs) and their integration with knowledge systems
Experience with GraphRAG, vector databases, and hybrid retrieval systems
Knowledge of semantic web technologies, RDF, and ontology engineering
Familiarity with agent-based systems and multi-agent architectures
Development Skills:
Proficiency in Python for graph database development
Experience with graph visualization tools and libraries
Understanding of distributed systems and microservices architecture
Familiarity with containerization (Docker, Kubernetes) and cloud platforms
Good to have Qualifications:
Experience with agentic AI systems, autonomous agents, or intelligent automation platforms
Background in knowledge representation, semantic reasoning, or expert systems
Contributions to open-source graph database or AI projects
Experience with real-time streaming data and dynamic knowledge graph updates
Understanding of business process modeling and workflow orchestration
What You'll Be Working On:
Building the knowledge backbone for next-generation business automation agents
Creating intelligent systems that can understand, reason about, and act within complex business contexts
Developing novel approaches to combine structured knowledge with generative AI capabilities
Contributing to our proprietary agentic frameworks that will transform how businesses implement AI-driven workflows
This role offers the opportunity to work at the forefront of agentic AI technology, combining cutting-edge graph database expertise with the exciting challenges of building truly intelligent autonomous systems for business applications.
Knowledge Graph, Knowledge Base, Graph Database, LLM, GraphRAG, Python, Neo4j, Gen AI (Langchain OR Langraph OR Crew AI OR Autogen)