Knowledge Engineer II

Job Title: Knowledge Engineer II

Location: Remote (USA)

Job Summary

We are seeking a highly skilled Knowledge Engineer II to join our team and contribute to the development of enterprise-scale Knowledge Graph solutions within the life sciences domain. The ideal candidate will possess expertise in ontology engineering, semantic web technologies, linked data, and biomedical ontologies. This role involves designing semantic data models, implementing knowledge graph architectures, supporting AI/LLM initiatives, and enabling advanced data integration for research and analytics.

Key Responsibilities

• Design, develop, and maintain enterprise Knowledge Graphs for life sciences and biomedical applications.
• Define semantic data models, ontology structures, entity relationships, and schema governance standards.
• Develop and maintain RDF-based knowledge models using semantic web technologies.
• Build ontology mappings and vocabulary alignment using industry-standard biomedical ontologies.
• Create and maintain SPARQL queries, OWL ontologies, SKOS vocabularies, SHACL constraints, and RML/R2RML mappings.
• Implement linked data principles and semantic interoperability across enterprise data platforms.
• Develop data serialization models using JSON Schema, LinkML, and related standards.
• Write Python scripts for ontology mapping, metadata enrichment, data validation, and automation.
• Support AI and LLM initiatives through entity linking, metadata enrichment, embedding pipelines, and Retrieval-Augmented Generation (RAG) solutions.
• Collaborate with data engineers, architects, and business stakeholders to build scalable semantic data solutions.
• Ensure data quality, governance, and consistency across knowledge graph implementations.
• Document technical designs, ontology standards, and best practices.

Required Qualifications

• Master''s degree in BioSciences, Biomedical Science, Biomedical Engineering, Biotechnology, or a related Life Sciences discipline.
• 6+ years of experience in Knowledge Graph development and semantic technologies.
• Hands-on experience with:
  • Knowledge Graph development
  • Entity modeling
  • Relationship design
  • R2RML
  • Schema governance
• Strong expertise in:
  • Protégé
  • SPARQL
  • OWL
  • SKOS
  • SHACL
  • RML
  • RDF-Star
• Working knowledge of biomedical and life sciences ontologies, including:
  • Gene Ontology (GO)
  • OBO Foundry Ontologies (CL, UBERON, HPO, MONDO, CHEBI, EFO, CLO)
  • MeSH
  • SNOMED CT
  • UMLS
• Strong understanding of linked data principles and Semantic Web technologies.
• Experience with JSON Schema, LinkML, or equivalent data serialization frameworks.
• Proficiency in Python for scripting, data modeling, automation, and quality validation.
• Experience supporting AI/LLM workflows and semantic data integration.

Preferred Qualifications

• Experience with data governance and data quality tools such as Ataccama, Informatica, Talend, OpenRefine, Great Expectations, or dbt.
• Experience building AI-ready knowledge ecosystems, including:
  • Metadata enrichment
  • Entity linking
  • Embedding pipelines
  • Retrieval-Augmented Generation (RAG)
• Understanding of vector databases such as Weaviate and Chroma.
• Experience with graph databases including Neo4j, Amazon Neptune, Stardog, GraphDB, or TigerGraph.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Experience designing scalable semantic data pipelines for enterprise life sciences applications.