Knowledge Graph Architect

Knowledge Graph Architect AI & Graph Analytics Platforms

Experience: 12 - 15 years

Core Expertise Requirements

Hands-on experience inbuilding, deploying, and scaling enterprise Knowledge Graph (KG) platformsin production environments. Demonstrated experience operating KGs at scale with a deep understanding ofperformance optimization, scalability challenges, and operational lessons learned from production deployments.

Strong experience in designing and implementingontology layers and semantic modelsfor Knowledge Graphs. Deep understanding ofontology design, schema evolution, and how semantic constructs support scalability, reasoning, and maintainability of large graph ecosystems.

Domain experience inKnowledge Graph development within Supply Chain ecosystemsincluding supplier networks, product relationships, and operational dependencies.Healthcare domain exposure will be considered an added advantage.

Key Responsibilities

Enterprise Knowledge Graph Architecture

Define and implemententerprise-scale Knowledge Graph architecturerepresenting entities, relationships, dependencies, and business context.

Designsemantic models, ontologies, and graph schemasfor enterprise data ecosystems.

Establishstandards, governance frameworks, and best practicesfor Knowledge Graph adoption across the organization.

Design scalableontology-driven graph architecturesthat 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 withdata lake / lakehouse platforms,APIs and microservices for AI applications

Cloud Platforms:AWS (preferred for Neptune-based architectures)

Required Skills

Deep expertise inKnowledge Graph architecture and semantic modeling

Strong background ingraph theory and network analytics

Enterprise architecture experience forAI-driven platforms

Graph analytics algorithms (centrality, clustering, similarity, link prediction)

Graph traversal and path analysis

Knowledge Graph integration withLLM and GraphRAG architectures

Strong background indata engineering and distributed data platforms

Preferred Experience

Enterprise Knowledge Graph implementations

Graph-basedsupply chain or ecosystem analytics platforms

AI copilots and enterprise knowledge assistants

Graph-based decision intelligence platforms

Experience withGraph Data Science libraries

Education:

Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics

Ideal Candidate

Atechnology leader with deep expertise in Knowledge Graphs, Graph Analytics, and AI architectures, capable of designingnext-generation GraphRAG platforms that combine Knowledge Graph intelligence with LLM-based reasoningto enableenterprise decision intelligence and advanced analytics across complex networked systems.