Key Responsibilities
* Ontology & Semantic Modeling: Design and maintain formal ontologies and semantic models (e.g., RDF, OWL) to accurately represent complex domain knowledge and relationships within a knowledge graph.
* Graph Architecture & Development: Build and optimize knowledge graph architectures using leading graph database technologies (e.g., Neo4j, JanusGraph, Amazon Neptune).
* Data Integration & ETL: Develop scalable data pipelines and ETL processes to ingest, transform, and map diverse data sources into graph structures while ensuring data quality and consistency.
* Querying & Analytics: Create advanced, high-performance graph queries (e.g., Cypher, SPARQL) and implement graph algorithms (e.g., shortest path, community detection) to deliver actionable insights.
* Cross-Functional Collaboration: Partner with Data Scientists, ML Engineers, Product Managers, and domain experts to translate business needs into technical solutions that power search, recommendations, and AI/ML applications.
* Performance Optimization: Monitor and tune graph database performance and associated pipelines for scalability and real-time query efficiency.
Essential Requirements
* 5+ years of experience designing and implementing large-scale knowledge graphs in production environments.
* Strong expertise in ontology engineering, semantic modeling, and familiarity with standards such as RDF, RDFS, OWL, and SPARQL.
* Proficiency with at least one major graph database (e.g., Neo4j, TigerGraph, AWS Neptune).
* Hands-on experience integrating LLMs with knowledge graphs (e.g., RAG, KG-powered AI agents).
* Solid programming skills, primarily in Python.
* Experience applying NLP techniques for text processing and structuring (e.g., NER, relation extraction).
* Strong problem-solving abilities and collaborative mindset in fast-paced, agile environments.
* Familiarity with graph algorithms and libraries (e.g., Neo4j GDS, NetworkX).
* Excellent communication skills to explain complex AI concepts to technical and non-technical audiences.
Desired Skills (Nice to Have)
* Advanced degree (Master’s or Ph.D.) in Computer Science, AI, or related technical field.
* Contributions to research or open-source projects.
* Experience with Google Cloud Platform and Vertex AI.
* Knowledge of NLP techniques for entity and information extraction to populate knowledge graphs.
* Familiarity with Graph Machine Learning (e.g., Graph Neural Networks).