Senior Knowledge Engineer

What you'll do

• Design and maintain enterprise-scale knowledge graphs, ontologies, and semantic data models to power agentic AI systems.
• Translate complex domain knowledge from subject matter experts into machine-readable structures using RDF, OWL, or property graph models.
• Establish and enforce data modeling standards, schema design patterns, and best practices for structured and semi-structured information.
• Lead discovery workshops with client stakeholders to identify, validate, and formalize requirements for domain knowledge.
• Integrate knowledge graphs as grounding and context layers for LLM-based agentic pipelines and RAG systems.
• Develop scalable pipelines for the population, enrichment, and lifecycle management of knowledge graphs in collaboration with data engineering teams.
• Act as a strategic advisor to clients by communicating complex ontological concepts to both technical and non-technical audiences.
• Evaluate and pilot emerging tools, frameworks, and standards such as Neo4j, Stardog, and W3C standards.

What we're looking for

• A Bachelor's degree is required, or an Associate's degree with at least 6 years of experience.
• Candidates without a Bachelor's degree must have at least 12 years of work experience.
• 4+ years of experience in Knowledge Graph technologies including RDF, SPARQL, Gremlin, LPG, SHACL, and RDFS are required.
• 2+ years of experience in schema design, ontology management, and Knowledge Graph curation are required.
• 2+ years of experience in semantic modeling and linked data principles are required.
• 2+ years of experience designing knowledge graph solutions and graph-based machine learning models are required.
• 2+ years of experience with relational databases, object stores, graph databases, and vector databases are required.
• 2+ years of experience in agentic pipeline design using LangChain, LlamaIndex, or AutoGen is required.
• Experience with cloud platforms (AWS, Azure, GCP) is preferred.
• Experience in Python with frameworks like TensorFlow, PyTorch, and ETL tools is preferred.
• Practical experience with NLP, enterprise search, prompt engineering, and LLM applications is preferred.
• A Ph.D. in a related field or broad experience in diverse ML techniques is preferred.