Design, develop, and scale enterprise knowledge graph solutions. Leverage healthcare standards and AI for clinical intelligence. Collaborate with clinicians and technical stakeholders.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Knowledge Graph Architect
Full-time
Fully Remote
Overview
We are seeking a highly skilled Knowledge Graph Architect to design, develop, and scale enterprise knowledge graph solutions with a strong focus on ontology engineering, healthcare semantics, and AI integration. This role will be instrumental in building semantic data layers that enable interoperability, clinical intelligence, and AI-driven insights.
You will leverage healthcare standards such as SNOMED CT and RxNorm alongside advanced semantic technologies and AI to support use cases like clinical decision support, patient 360, and intelligent data integration across healthcare systems.
Key Responsibilities:
Knowledge Graph Architecture
- Design and implement scalable knowledge graph solutions using RDF, property graphs, or hybrid approaches
- Define architecture patterns for integrating clinical, claims, and operational data
- Evaluate and implement graph platforms (e.g., Neo4j, Amazon Neptune, Stardog, GraphDB)
Ontology & Semantic Modeling (Healthcare Focus)
- Develop and maintain enterprise ontologies and healthcare semantic models
- Incorporate and map clinical terminologies such as:
- SNOMED CT for clinical concepts and relationships
- RxNorm for normalized drug vocabularies and medication data
- Align with standards such as FHIR, LOINC, ICD-10, and HL7
- Use semantic standards: OWL, RDF, RDFS, SKOS, SHACL
- Establish ontology governance, versioning, and lifecycle management
AI & Machine Learning Integration
- Integrate knowledge graphs with AI/ML and NLP pipelines for clinical data
- Enable use cases such as:
- Clinical concept extraction from unstructured notes
- Entity resolution and patient matching
- Drug–condition and drug–drug relationship modeling
- Support LLMs and generative AI systems with knowledge graph grounding and RAG (Retrieval-Augmented Generation)
- Design pipelines to extract structured knowledge from EHRs and clinical documentation
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Healthcare Data Integration
- Build pipelines to ingest data from:
- Electronic Health Records (EHRs)
- Claims systems
- Lab and pharmacy systems
- Map and normalize data using SNOMED CT and RxNorm mappings
- Ensure data quality, lineage, and provenance tracking
Querying & APIs
- Develop and optimize queries using SPARQL, Cypher, or Gremlin
- Enable semantic search across clinical and pharmaceutical data
- Build APIs for integration with clinical applications and analytics platforms
Governance & Compliance
- Establish semantic governance aligned with healthcare regulatory requirements (HIPAA, interoperability rules)
- Define standards for terminology usage (SNOMED CT, RxNorm) across systems
- Ensure consistent metadata management and semantic quality
Collaboration & Leadership
- Partner with clinicians, data scientists, informaticists, and engineers
- Provide technical leadership in semantic technologies and healthcare data standards
- Translate clinical requirements into scalable semantic models
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Health Informatics, Data Science, or related field
- 5+ years of experience in knowledge graph development or semantic technologies
- Strong expertise in:
- RDF, OWL, SPARQL
- Ontology modeling and semantic frameworks
- Hands-on experience with:
- SNOMED CT and RxNorm implementation or mapping
- Healthcare data standards (FHIR, HL7, ICD, LOINC)
- Experience with graph databases (Neo4j, Stardog, Amazon Neptune, etc.)
- Proficiency in Python, Java, or Scala
- Experience integrating AI/ML or NLP with structured knowledge systems
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Preferred Qualifications
- Experience in healthcare, life sciences, or pharmaceutical domains
- Familiarity with clinical data models and EHR systems (Epic, Cerner, etc.)
- Experience with LLMs, generative AI, and RAG architectures
- Knowledge of terminology management tools and ontology editors (Protégé, TopBraid)
- Experience with cloud platforms (Azure Health Data Services, AWS HealthLake, GCP Healthcare API)
Key Skills
- Healthcare ontology engineering & semantic interoperability
- SNOMED CT and RxNorm modeling and integration
- Knowledge graph design and graph databases
- AI/ML + NLP integration with clinical data
- Data governance and regulatory awareness
- Strong collaboration with clinical and technical stakeholders
What Success Looks Like
- A scalable clinical knowledge graph integrating multi-source healthcare data
- Effective use of SNOMED CT and RxNorm for semantic interoperability
- AI-driven applications powered by high-quality, structured clinical knowledge
- Improved clinical insights, decision support, and patient outcomes
Sample Use Cases
- Patient 360 with unified clinical and medication view
- Drug interaction and clinical decision support systems
- AI-powered clinical search and question answering
- Population health analytics and risk modeling
- Knowledge grounding for healthcare LLM applications
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