Quick Overview
Job Description
AI Lead / AI Architect
Healthcare | AWS Bedrock | Agentic AI
Domain Healthcare / Medicaid
Level Senior / Lead Role Type AI Architecture / Technical Lead
Role Summary
We are looking for a senior AI Lead / Architect to lead the design and implementation of an enterprise Agentic AI solution
for a healthcare client. The role requires strong hands-on experience with AWS Bedrock, GenAI/LLM architecture, agent
orchestration, enterprise data integration, and customer-facing technical leadership.
Must-Have Skills
Strong hands-on experience with AWS Bedrock.
Experience designing Generative AI / Agentic AI solutions.
Experience with LangGraph or a similar agent orchestration framework.
Strong understanding of LLM agents, tool/function calling, RAG, prompt engineering and context engineering.
Experience integrating AI solutions with APIs and enterprise data platforms.
Strong solution architecture and customer-facing technical leadership skills.
Healthcare domain experience.
Understanding of security, PHI/PII, access control and AI governance.
Strongly Preferred
Medicaid, Pharmacy or PBM experience.
Experience leveraging Databricks Unity Catalog as the context/metadata plane for AI systems, with Databricks
Medallion Architecture as the governed data source for GenAI / Agentic AI solutions.
Amazon Bedrock AgentCore.
MCP (Model Context Protocol).
Natural Language-to-SQL / conversational analytics.
Multi-agent / supervisor-agent architecture.
Key Responsibilities
Own the end-to-end AI solution architecture.
Lead AWS Bedrock and Agentic AI implementation.
Design agent workflows and enterprise data integrations.
Lead and guide AI Engineers through design and implementation.
Work directly with client architecture, engineering and business teams.
Ensure scalability, security, governance, observability and cost optimization.
TA Screening Priority: Prioritize candidates with the combination of AWS Bedrock + Agentic AI + Healthcare.
Databricks/Unity Catalog and Medicaid/Pharmacy/PBM should be treated as strong preferences rather than strict elimination criteria.
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