Key Responsibilities
· Define end-to-end Agentic AI and Knowledge Graph architecture, including multi-agent, planner, supervisor, tool-use, and human-in-the-loop patterns.
· Architect integrations with Databricks, Unity Catalog, Orkes workflows, and enterprise data platforms.
· Implement RAG and hybrid retrieval using graph, vector, and structured data.
· Establish AI security and governance standards covering HIPAA, PHI, tenant isolation, grounding, explainability, evaluation, and observability.
· Guide reusable AI agent capabilities for data discovery, Epic mapping, data quality, remediation, and workflow automation.
· Review architecture, technical deliverables, and production-readiness while providing technical guidance to development teams.
Required Skills
· 12–15 years of experience in AI/ML, Data, or Enterprise Architecture.
· Strong hands-on experience with Stardog, Agentic AI, Knowledge Graphs, GenAI, and ML.
· Experience with multi-agent orchestration, RAG, graph-based retrieval, and human-in-the-loop solutions.
· Strong knowledge of healthcare data, HIPAA, PHI, EHR/Epic, FHIR, and HL7.
· Experience with Azure, Databricks, PySpark, MLflow, Unity Catalog, Python, SQL, REST APIs, and microservices.
· Strong understanding of data engineering, metadata, lineage, data quality, security, and cloud architecture.
· Excellent communication, stakeholder management, and architecture-governance skills.
Education
· Bachelor's or Master's degree in Engineering, Computer Science, or a related technical field.
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