Knowledge Graph Architect AI & Graph Analytics Platforms
Experience: 12 15 Years (C5-C6)
Core Expertise Requirements
- Hands-on experience in building, deploying, and scaling enterprise Knowledge Graph (KG) platforms in production environments. Demonstrated experience operating KGs at scale with a deep understanding of performance optimization, scalability challenges, and operational lessons learned from production deployments.
- Strong experience in designing and implementing ontology layers and semantic models for Knowledge Graphs. Deep understanding of ontology design, schema evolution, and how semantic constructs support scalability, reasoning, and maintainability of large graph ecosystems.
- Domain experience in Knowledge Graph development within Supply Chain ecosystems including supplier networks, product relationships, and operational dependencies. Healthcare domain exposure will be considered an added advantage.
Key Responsibilities
Enterprise Knowledge Graph Architecture
- Define and implement enterprise-scale Knowledge Graph architecture representing entities, relationships, dependencies, and business context.
- Design semantic models, ontologies, and graph schemas for enterprise data ecosystems.
- Establish standards, governance frameworks, and best practices for Knowledge Graph adoption across the organization.
- Design scalable ontology-driven graph architectures that support long-term extensibility and reasoning capabilities.
Technology Architecture
Graph Databases
Lead Architecture And Evaluation Of Enterprise Graph Platforms
- Amazon Neptune, Neo4j, ArangoDB
Graph Data Models
- RDF (Resource Description Framework), Labeled Property Graph (LPG)
Graph Query Languages
- Cypher, SPARQL, Gremlin, ArangoQL
AI & LLM Ecosystem
Experience Designing AI Platforms Using
LLM Platforms: OpenAI / Azure OpenAI, Anthropic Claude
LLM Frameworks: LangChain, LangGraph, LlamaIndex
Data & Platform Engineering
Snowflake, Python / Java / Scala, Data engineering pipelines, ETL / ELT frameworks, Integration with
data lake / lakehouse platforms, APIs and microservices for AI applications
Cloud Platforms: AWS (preferred for Neptune-based architectures)
Required Skills
- Deep expertise in Knowledge Graph architecture and semantic modeling
- Strong background in graph theory and network analytics
- Enterprise architecture experience for AI-driven platforms
- Graph analytics algorithms (centrality, clustering, similarity, link prediction)
- Graph traversal and path analysis
- Knowledge Graph integration with LLM and GraphRAG architectures
- Strong background in data engineering and distributed data platforms
Preferred Experience
- Enterprise Knowledge Graph implementations
- Graph-based supply chain or ecosystem analytics platforms
- AI copilots and enterprise knowledge assistants
- Graph-based decision intelligence platforms
- Experience with Graph Data Science libraries
Education
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics
Ideal Candidate
A
technology leader with deep expertise in Knowledge Graphs, Graph Analytics, and AI architectures, capable of designing
next-generation GraphRAG platforms that combine Knowledge Graph intelligence with LLM-based reasoning to enable
enterprise decision intelligence and advanced analytics across complex networked systems.
Neo4j Graph DB,Knowledge Graph,semantic modeling,graph theory,GraphRAG,LLM,Data Engineering
