Graph RAG - Knowledge Engineer

Skills & Expectations

• 5–9 years of software engineering experience, including hands-on experience building production knowledge graphs and applied NLP systems.
• Strong Python development skills, with experience building production APIs and services using Python and FastAPI.
• Strong expertise in entity and relation extraction, Named Entity Recognition (NER), entity resolution/matching and semantic normalization.
• Experience designing ontologies, taxonomies and graph schemas to represent complex business and domain relationships.
• Hands-on experience with Neo4j and/or Amazon Neptune, including Cypher/openCypher for graph querying and traversal.
• Practical experience building GraphRAG and graph + vector hybrid retrieval solutions for AI/GenAI applications.
• Experience designing and optimizing graphs at millions-of-nodes/edges scale, with focus on query performance, data quality and scalability.
• Strong understanding of building production data pipelines using AWS S3, ECS/EKS and Bedrock, with Docker, pytest, Git and CI/CD.
• Biomedical ontology experience with standards such as UMLS, MeSH, SNOMED, RxNorm, ICD-10, DrugBank and/or ChEMBL is highly preferred.
• Pharma, Life Sciences or Healthcare domain experience is preferred, particularly experience working with scientific, clinical or drug-related data.
• Expected to bring strong ownership across the lifecycle—from NLP extraction and graph construction through retrieval, GenAI integration, evaluation and production operations.