Agentic AI Engineer

Required Skills & Experience

Experience \n• 3 years of experience in a Machine Learning Engineering, Data Science or Data \nEngineering role \n• 2 year of experience in implementing machine learning algorithms in a production \nenvironment or applying software development lifecycle principles to analytics \n• Hands-on experience designing, building, or integrating LLM-based or agentic \napplications (e.g., LLM-based applications, RAG systems, or tool-using agents) in a \nproduction or pilot setting, including retrieval over enterprise knowledge, tool/function \ncalling, workflow handoffs, and secure integration with enterprise systems.

Nice to Have Skills & Experience

Preferred Qualifications (preferred experience, education, certifications) \n• Experience building agentic or LLM applications using orchestration frameworks (e.g., \nLangGraph, LangChain, Semantic Kernel) and Azure Foundry and OpenAI / OpenAI APIs \n• Experience engineering AI-enabled applications or services that integrate APIs, backend \nservices, authentication/authorization, enterprise identity, user-facing interfaces, and \nplatform services in a secure production environment. \n• Experience making generative/agentic systems safe for regulated use: evaluation \nharnesses and guardrails (e.g. evaluation harnesses, safety filtering, , permission-aware \nresponses, identity verification, session isolation, and auditable configuration patterns). \n• Experience with vector stores / embeddings and RAG pipelines for domain-specific \napplications \n• Experience working with healthcare data (payer or provider) in a HIPAA-regulated \nenvironment \n• Epic certification or badges (e.g., Cogito, Cognitive Computing Platform, Chronicles, \nInterconnect) \n• Azure certifications (e.g., AI Engineer Associate, AI Fundamentals, Data Science, or Data \nEngineering)

Job Description

The Agentic AI / ML Engineer builds, deploys, and operates production AI and machine learning \nsystems for clinical and operational use across the pediatric health system. The role spans both \npredictive machine learning (ML) models and generative and agentic AI — LLM-based \napplications, RAG, copilots, and workflow agents — and owns the evaluation, guardrail, and \nmonitoring frameworks that make these systems safe and reliable to run in a regulated \nenvironment. Working closely with data scientists, clinical informaticists, and EHR/application \nteams, this role moves solutions from prototype into production and serves as a technical lead \nfor agentic AI/ML delivery — setting the standards other engineers and data scientists build \nagainst, and leading incident response for production systems. The role also helps define \nenterprise AI system patterns for integrating foundation models into production applications, \nincluding tool/function calling, retrieval over enterprise knowledge, permission-aware \nresponses, system handoffs, conversation/session management, and reusable configuration \npatterns for AI-enabled workflows.