As an AI Architect, you will be a part of an Agile team to build healthcare applications and implement new features while adhering to the best coding development standards.
Mandatory skills
- Stardog, Agentic AI, Knowledge Graph, ML
Responsibilities: -
- Define the end-to-end Agentic AI and Knowledge Graph architecture.
- Design supervisor, planner, specialized agent, tool-use, and human-in-the-loop patterns.
- Define integration with VIP data platform, Orkes workflows, Databricks, and Unity Catalog.
- Design the Stardog Knowledge Graph model for schemas, mappings, recipes, rules, lineage, and contractual controls.
- Establish AI security, HIPAA, PHI protection, tenant isolation, and contract-honoring guardrails.
- Define standards for grounding, explainability, confidence scoring, evaluation, and observability.
- Guide reusable agent skills for source discovery, Epic mapping, recipe generation, quality, and remediation.
- Review solution designs, technical deliverables, and production-readiness.
Skills Required
- Enterprise architecture for Agentic AI, GenAI, ML, and data platforms.
- Multi-agent orchestration and human-in-the-loop architecture.
- Knowledge Graph, ontology, semantic modeling, and graph-based retrieval.
- RAG and hybrid retrieval using graph, vector, and structured data.
- Healthcare data, HIPAA, PHI, EHR/Epic, and clinical-data integration knowledge.
- Data engineering, metadata management, lineage, and data-quality concepts.
- API, microservices, event-driven, security, and cloud architecture.
- Strong stakeholder communication and architecture-governance skills.
Tool & Technology Exposure
Azure AI Foundry, LangChain, DeepAgents, Stardog, Orkes, Azure Databricks and PySpark, MLflow Unity Catalog Azure Blob Storage Azure Document Intelligence Dynatrace and OpenTelemetry GitHub and CI/CD Python, SQL, REST APIs, FHIR, HL7