Senior Artificial Intelligence Engineer

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).