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Humboldt Park Health · 9 hours ago

Ai Engineer

Chicago, IL

Full-time

Onsite

Mid Level

Humboldt Park Health is focused on integrating advanced AI solutions in healthcare. The AI Engineer will partner with various stakeholders to develop and implement decision-support tools and machine learning services, ensuring compliance and security in handling patient information.

Health CareHealth DiagnosticsHospitalMedical

H1B Sponsor Likely

Responsibilities

Designs, integrates, and supports CDS and AI solutions within the MEDITECH EHER environment, including clinical documentation workflows, order management, results review, and interoperability interfaces

Designs, builds, and maintains CDS applications integrated with Humboldt Park Health’s EHR and clinical workflows (e.g., triage, order entry, discharge, chronic care follow-up)

Implements real-time CDS interoperability patterns (e.g., CDS Hooks / SMART on FHIR where applicable) and develops secure APIs/services for decision support

Develops, validates, and deploys machine learning models for clinical and operational use cases (e.g., ED risk stratification, readmission risk, diabetes care gaps, wound care risk flags)

Builds and maintains data pipelines for clinical data ingestion, data quality checks, feature engineering, and dataset/version control while protecting PHI

Applies explain ability methods (e.g., SHAP/LIME) and human-centered design to ensure outputs are interpretable, actionable, and reduce alert fatigue

Partners with clinicians and governance stakeholders to define requirements, acceptance criteria, validation plans, and clinical safety guardrails

Produces technical documentation for build, validation, and change control; supports audit readiness and aligned documentation when applicable

Ensures compliance with HIPAA, least-privilege access controls, security standards, and hospital data governance; participates in incident response as needed

Collaborates with vendors, community partners, and internal teams to integrate and support CDS/AI solutions across on-site and mobile care settings

Adheres to established departmental policies, procedures, and objectives

Performs both essential and non-essential job functions safely as identified by Humboldt Park Health

Demonstrates an understanding of and models the mission and core values of Humboldt Park Health

Enhances professional growth and development by accessing educational programs, job-related literature, in-service meetings, and workshops/seminars

Performs other related duties as assigned

Qualification

Large Language Models (LLM)Clinical Decision Support (CDS)Machine LearningHIPAA ComplianceData Pipeline DevelopmentHuman-Centered DesignFeature EngineeringSecurity StandardsEducational ProgramsTechnical DocumentationCollaboration

Required

Partners with clinicians, informatics, and IT to translate workflows and evidence-based practices into secure, explainable, and compliant decision-support tools, including real-time in-workflow CDS integrations, LLM-enabled clinical assistance (e.g., summarization, guideline interpretation, clinical text analysis), and production-grade machine learning services

Accountable for end-to-end delivery: requirements, build, validation, deployment, monitoring, and continuous improvement - aligned with Humboldt Park Health's mission

Ensures secure handling of PHI when using LLMs, including on-premise or HIPAA-compliant deployment patterns and vendor risk assessments

Implements safeguards for LLM use in healthcare, including prompt governance, output validation, and audit logging

Designs, fine-tunes, evaluates, and deploys Large Language Model (LLM) solutions for clinical use cases such as clinical note summarization, clinical guideline interpretation, patient education content, and decision-support augmentation

Designs, builds, and maintains CDS applications integrated with Humboldt Park Health's EHR and clinical workflows (e.g., triage, order entry, discharge, chronic care follow-up)

Implements real-time CDS interoperability patterns (e.g., CDS Hooks / SMART on FHIR where applicable) and develops secure APIs/services for decision support

Develops, validates, and deploys machine learning models for clinical and operational use cases (e.g., ED risk stratification, readmission risk, diabetes care gaps, wound care risk flags)

Builds and maintains data pipelines for clinical data ingestion, data quality checks, feature engineering, and dataset/version control while protecting PHI

Applies explainability methods (e.g., SHAP/LIME) and human-centered design to ensure outputs are interpretable, actionable, and reduce alert fatigue