AI / ML Engineer
7 days ago
Role & responsibilities
Integrate agents with EHRs and healthcare systems (FHIR/HL7 APIs, scheduling, messaging platforms, CRM/engagement tools) under strict access controls and audit logging.
Collaborate with Product Manager and Architects.
Define and run evaluation protocols specific to healthcare (clinical correctness, hallucination risk, safety events, equity, healthliteracy suitability) and iterate on prompts, tools, and models.
Implement monitoring, guardrails, and incident response processes for AI behavior in production, including PHI handling, safety fallbacks, and humanintheloop review.
Required qualifications
56 years of AI/ML engineering experience, including building production services or data/MLbacked applications.
Strong Python(including fast api, flask) skills and experience building backend services, APIs, and integrations; familiarity with cloud environments (AWS/Azure/GCP) and containerization (Docker/Kubernetes).
Handson experience with LLMs and agent frameworks (e.g., LangChain, LangGraph, LlamaIndex, Semantic Kernel) including tool calling and orchestration.
Handson experience with ReactJS and PostgressDB, any VectorDB.
Demonstrated experience building LLM or AI solutions for healthcare or other regulated industries, or strong familiarity with medical data types and workflows.
Understanding of healthcare privacy and compliance concepts (HIPAA, PHI, rolebased access, audit trails) and how they impact system design.
preferred:
Proven ability to work crossfunctionally with clinical and nontechnical stakeholders and to document designs and limitations clearly.
Preferred candidate profile
Experience with clinical NLP tasks (ICD10 or CPT extraction, note summarization, guideline matching, patientfriendly transformations, prior auth support, etc.).
Experience integrating with or building around EHR systems, FHIR resources, HL7 messaging, or digital health products (telehealth, RPM, patient portals).
Background in ML or data science, especially in healthcare datasets (claims, EHR, imaging, device data) and model evaluation in clinical contexts.
Familiarity with clinical safety frameworks for AI (e.g., model cards, risk assessments, bias/equity analyses, human factors in clinical workflows).
Education and soft skills
Bachelors or Masters degree in Computer Science, Biomedical Informatics, Engineering, or related field, or equivalent practical experience.
Excellent written and verbal communication, able to bridge clinical and technical language and to present tradeoffs to clinicians and leadership.
Missiondriven mindset and empathy for patients and providers; commitment to building safe, equitable, and trustworthy healthcare AI systems.
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