OverviewAs an AI Engineer, you will design, develop, and deploy AI-powered services across Azure AI Resources including: document intelligence center, AI Search, Azure Agent Service, and retrieval-augmented generation (RAG) pipelines. You’ll implement agentic frameworks and use Model Context Protocol (MCP) to define and expose tools/APIs so agents can safely invoke them within multi-agent workflows. Working with cross-functional teams, you’ll help translate clinical, member and operational needs into scalable, compliant (e.g., HIPAA) AI solutions following all applicable leangle, compliance, AI Governance, and Security requirements.Key ResponsibilitiesAgentic Architecture & DesignDesign agentic frameworks for multi-agent orchestration; use MCP specifically to declare tool schemas/connectors and enable safe tool/API invocation (state and task coordination are handled by the orchestrator, not MCP).Help design AI solutions using Azure AI Services (AI Foundry, Document Intelligence Center, AI Search, Open AI, Azure Agent Service).Design secure RAG pipelines leveraging LLM inference and internal knowledge sources.API Integration & DevelopmentHelp design and build APIs to deliver AI capabilities to systems like Chatbot, AI Applications, EHR and member portals.Integrate clinical/member/operational APIs (e.g., FHIR, scheduling systems) with AI pipelines.ML Engineering & ImplementationWrite production-grade Python, .Net code for model orchestration, data ingestion, and endpoint development.Tune prompts and optimize OpenAI/LLM performance for clinical content.Build chatbots with AI Foundry, Azure Agent Service and integrate OCR/Key-Value extraction via Azure Document Intelligence Center.Deployment & OperationsContainerize and deploy AI microservices (Docker, Kubernetes, Azure App Services).Implement CI/CD pipelines and monitor models for drift, system health, and performance using observability tools.Governance & DocumentationCollaborate with Security, Legal, Compliance and AI Governance teams to ensure Responsible AI practices and HIPAA compliance.Maintain solution documentation, runbooks, and technical specifications.Education :Must: Highschool, GEDPreferred: MS or PhD in Computer Science, AI/ML, Mechatronics, Electrical Engineering, or a related field.Certification/LicensureNo specific certification or licensure requirementsRequired Qualifications3+ years of hands-on experience in production-grade AI/ML development.Strong Python skills with TensorFlow/PyTorch.Deep experience with Azure AI (AI Foundry, Document Intelligence Center, AI Search, OpenAI, Azure Agent Service).Skilled in RAG pipelines, LLM prompt engineering, and multi-agent orchestration (with MCP used for tool integration).Proficient in REST/gRPC APIs, Docker, Kubernetes, and CI/CD tools (GitHub Actions, Azure DevOps).Clear communicator with the ability to engage cross-functional teams.Preferred QualificationsMicrosoft Azure AI Engineer CertificationHealthcare/life sciences experience.Experience with ML registries (MLflow, Azure ML), A/B testing, and chatbot analytics.Familiarity with OpenAI fine-tuning (GPT-3.x/4), encryption, and privacy-preserving methods.Open-source contributions or published work in AI/ML.

We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for Full Time employment is:$91,416.00-$152,380.80. Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities.