Descripción
Key Responsibilities : Design, develop, and maintain an AI agents leveraging RAG and autonomous AI agents, enabling clinical researchers to query sensitive federated datasets using natural language Build and iterate on an interoperable query language to SQL generation pipeline, including schema-awareness layers, and query validation tailored to complex clinical data models Implement and maintain data harmonization pipelines conforming to the OMOP Common Data Model and HL7 FHIR standard, enabling interoperability across heterogeneous clinical datasets from multiple institutions Integrate and maintain medical ontology mappings across ICD-10, SNOMED CT, ATC, and RxNorm to ensure semantic consistency in federated patient data queries Develop and maintain full-stack platform features: from backend services, APIs, and query orchestration pipelines through to user-facing interfaces for clinical researchers and data scientists. What We Are Looking For AI/LLM Systems: Hands-on experience building production RAG pipelines and agentic AI workflows applied to clinical data querying. Medical Ontologies: Practical experience integrating a broad range of clinical ontologies and vocabularies into a query or data harmonization system, including but not limited to SNOMED CT, ATC, RxNorm, ICD-10, LOINC, and OMOP standard vocabularies. The candidate must demonstrate hands-on implementation experience managing concept mappings, hierarchies, and cross-ontology relationships. Backend Engineering: Solid Go development experience, including API design and data pipeline construction. Frontend Engineering: React development experience, including component design and building secure, clinical-facing user interfaces. Strong SQL expertise: Writing and optimizing complex queries and investigating, monitoring, and troubleshooting the performance of production databases such as PostgreSQL and Oracle. Preferred qualifications Experience working with relational databases and complex clinical data mod