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Knowledge Graph Engineer Jobs in Pennsylvania (NOW HIRING)

AWS AgentCore Platform Engr Location: Reading, PA (Hybrid 2-3 days/wk) Interview: Virtual ... Implementation of Knowledge Graph * Implementation of MCP servers in Agentcore * Implementation of ...

... . Cloud Expertise: Deep proficiency in AWS (IAM, CloudWatch, Bedrock, Lambda). Observability Tools ... • Implementation of Knowledge Graph • Implementation of MCP servers in Agentcore • ...

$57/hr

Construct/maintain a knowledge graph that links every recommendation back to source papers (full ... Strong engineering hygiene (tests, docs, code review) and product sense. * Clear, direct ...

Be a key contributor to the design and implementation of a scalable knowledge graph infrastructure ... Programming background in parser combinators, natural language processing, and linked data (RDF ...

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Infographic showing various Knowledge Graph Engineer job openings in Pennsylvania as of August 2026, with employment types broken down into 100% Full Time. Highlights an 33% In-person, and 67% Remote job distribution.

Enterprise Solutions Graph Database

H R PUNDITS INC

Collegeville, PA • On-site

Full-time

Re-posted 8 days ago


Job description

Job title : Enterprise Solutions Graph Database/ Architect
 Location: Upper Providence Township, PA
 (Onsite/Remote )
 Experience : 15 Years
Role Overview
 Seeking a seasoned Graph Database
 Knowledge Graph Expert to perform a comprehensive study of our existing platform. Evaluate our current architecture, data ontology, and query performance to provide a strategic roadmap. The goal is to evolve our Knowledge Graph into a robust, scalable engine that accelerates different Pharma areas ( drug discovery, clinical insights, and cross-departmental data democratization)
 
Required Qualifications
 Graph Expertise: 10+ years of experience with Graph Databases. Deep proficiency in LPG (Labeled Property Graphs) or RDF/Triple Stores.
 Pharma Domain Knowledge: Proven experience handling biomedical data types (e.g., Gene-Disease associations, Chemical compounds, Patient journeys).
 Semantic Web Standards: Strong understanding of Linked Data principles, URI strategies, and ontology modeling.
 Data Engineering: Experience with ETL/ELT pipelines that feed graphs from unstructured (PDF publications) and structured (EDC, LIMS) sources.
 Advanced Analytics: Experience implementing Graph Data Science algorithms (centrality, community detection) or integrating Graphs with Machine Learning.
 Technical Stack Preferences
 Graph DBs: AnzoGraph, Neo4j, Stardog,
 Languages: Python, Java, SPARQL, Cypher, or Gremlin.
 Bio-Ontologies: Familiarity with OBO Foundry, ChEMBL, or Ensembl.