Knowledge Graph Engineer/Architect

Role Overview: We are looking for an experienced Knowledge Graph Engineer/Architect to design, build, and scale our enterprise knowledge graph and semantic data platforms. The ideal candidate has strong experience in knowledge representation, graph databases, ontology modeling, and integrating structured/unstructured data to power search, reasoning, and intelligent applications. Key Responsibilities • Design and develop knowledge graphs, ontologies, and taxonomies aligned with business domains. • Build semantic models using RDF, OWL, SHACL, or property graph approaches. • Implement and maintain graph databases (e.g., Neo4j, AWS Neptune, Azure Cosmos DB Gremlin API, TigerGraph). • Ingest heterogeneous datasets and convert them into graph structures using ETL pipelines. • Work closely with product, data, and engineering teams to understand requirements and map real-world entities into graph models. • Develop and optimise SPARQL, Cypher, or Gremlin queries for graph analytics. • Integrate the knowledge graph with downstream systems (search, RAG pipelines, analytics, APIs). • Ensure data quality, entity resolution, schema alignment, and consistency across sources. • Implement reasoning, inference, metadata enrichment, and graph-based recommendations. • Conduct POCs, evaluate graph technologies, and define best practices for knowledge graph architecture. Required Skills & Experience • 3–10+ years of experience in data engineering, semantic technologies, or knowledge graphs. • Strong understanding of graph theory, ontologies, and linked data principles. • Hands-on experience with at least one major graph database: • Neo4j • AWS Neptune • Azure Cosmos DB (Gremlin) • TigerGraph • Expertise in SPARQL, Cypher, or Gremlin. • Experience with Python or Java for graph data ingestion and pipeline development. • Knowledge of ETL/ELT, data modeling, schema design, and API integration. • Familiarity with modern AI/ML workflows, especially RAG (Retrieval-Augmented Generation) or LLM-driven applications is a plus. • Understanding of semantic web standards (RDF/OWL), entity resolution, and graph embeddings. Nice-to-Have Skills • Experience with vector databases and hybrid search. • Exposure to LLM orchestration, RAG frameworks, or knowledge-grounded AI systems. • Background in enterprise domains such as retail, healthcare, insurance, fintech. • Familiarity with tools like GraphQL, LangChain, Kafka, Airflow, Databricks, or Azure Data Factory. • Knowledge of knowledge graph visualization tools (Bloom, GraphXR). Soft Skills • Strong analytical and problem-solving ability. • Ability to translate business needs into data models. • Excellent communication and cross-functional collaboration. • Ownership mindset with ability to work in ambiguous environments. Education • Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, or related field. • Certifications in semantic technologies or graph databases are a plus.