Senior Systems Analyst - AI models & Solutions (NGEMR)

Position Overview

The Epic Nebula AI / Machine Learning Engineers design, deploy, integrate, and support artificial intelligence and machine learning solutions within the Epic ecosystem, working closely with clinicians, operational stakeholders, data scientists, Epic application teams, integration teams, security teams, and infrastructure teams to translate business and clinical requirements into safe, reliable, and scalable AI-enabled workflows; responsibility includes deploying predictive or machine learning models through Epic Nebula and the Epic Cognitive Computing framework, integrating model inputs and outputs into Epic workflows, and ensuring that deployed models meet organizational requirements for performance, security, privacy, explainability, clinical safety, and ongoing monitoring, a role that requires both technical expertise in machine learning engineering and a strong understanding of healthcare workflows, Epic applications, clinical data, and production support processes.

Role & Responsibilities

• Meeting with clinicians, business stakeholders, Epic application analysts, data scientists, and subject matter experts to understand AI and predictive analytics requirements
• Evaluating whether proposed use cases are suitable for machine learning, generative AI, rules-based logic, or existing Epic functionality
• Translating clinical and operational requirements into technical model-integration specifications
• Preparing, validating, and transforming Epic clinical and operational data for model development and inference
• Deploying internally developed or third-party predictive models through Epic Nebula and the Epic Cognitive Computing framework
• Developing and maintaining model input mappings, output mappings, configuration, APIs, and workflow integration components
• Integrating model predictions, risk scores, classifications, recommendations, or generated content into appropriate Epic workflows
• Working with Epic application teams to configure user-facing components such as alerts, decision-support activities, work queues, patient lists, dashboards, or other workflow touchpoints
• Collaborating with data scientists to package models and ensure that model artifacts meet Epic deployment requirements
• Validating model compatibility, dependencies, input schemas, output schemas, and runtime requirements
• Performing unit testing, integration testing, workflow testing, performance testing, regression testing, and user acceptance testing
• Assessing model accuracy, calibration, sensitivity, specificity, false-positive rates, false-negative rates, fairness, and operational impact
• Establishing monitoring for model availability, latency, data quality, prediction distribution, model drift, and workflow adoption
• Investigating production issues involving model execution, data availability, interfaces, workflow configuration, or prediction delivery
• Maintaining model versioning, deployment records, technical documentation, validation evidence, and change-control documentation
• Supporting model promotion across development, test, validation, and production environments
• Participating in Epic upgrades and reviewing changes that may affect Nebula, cognitive computing, data structures, interfaces, or embedded AI workflows
• Ensuring compliance with organizational policies relating to cybersecurity, patient privacy, clinical safety, responsible AI, and data governance
• Supporting periodic model review, recalibration, retraining, rollback, retirement, and replacement
• Providing technical guidance and knowledge transfer to Epic analysts, data scientists, application support teams, and operational users

The position shall also be responsible for:

• External models integrated with Epic
• Real-time and batch inference workflows
• Generative AI and large language model integrations
• Epic Cognitive Computing configuration
• Epic Nebula model deployment and administration
• Model monitoring dashboards
• Feature engineering and reusable feature pipelines
• Integration with Clarity, Caboodle, Chronicles, Cosmos, FHIR, or other approved clinical data sources
• Integration with cloud-based AI or machine learning services
• Clinical decision-support configuration
• AI governance and model inventory management
• Evaluation of third-party healthcare AI products

Requirements

• A degree in computer science, data science, artificial intelligence, engineering, statistics, information systems, biomedical informatics, or a related discipline
• Strong programming skills in Python or another language commonly used for machine learning development
• Proficiency with SQL and relational database concepts
• Practical understanding of machine learning development, validation, deployment, and monitoring
• Experience working with model-serving APIs, JSON, RESTful services, authentication, and system integration
• Knowledge of software development lifecycle, version control, automated testing, release management, and production support
• Understanding of model performance measures such as precision, recall, sensitivity, specificity, calibration, and area under the curve
• Ability to identify data leakage, bias, model drift, unstable features, and inappropriate model use
• Strong analytical, troubleshooting, and problem-solving skills
• Strong written and verbal communication skills
• Ability to explain technical model behaviour and limitations to clinical and non-technical stakeholders
• Ability to understand complex healthcare workflows and translate them into technical requirements
• Ability to work effectively across clinical, operational, data, application, infrastructure, cybersecurity, and governance teams
• Understanding of healthcare data privacy, security, clinical safety, and responsible-AI principles
• Ability to maintain accurate technical, validation, deployment, and operational documentation
   

Preferred Epic-related competencies

• Familiarity with Epic applications, workflows, and data structures
• Familiarity with Epic Cognitive Computing, Nebula, or model deployment within Epic is strongly preferred
• Experience with Epic Clarity, Caboodle, Chronicles, or Cogito is an advantage
• Experience with Epic integration technologies, APIs, or FHIR is an advantage
• Ability to understand how model outputs affect clinician and operational workflows
• Experience supporting Epic upgrades, testing cycles, change control, and production releases is desirable
• Epic certification or proficiency in an applicable module is advantageous
• Certification or experience in Clarity Data Model, Caboodle Data Model, Cogito, Cognitive Computing, or relevant clinical applications is a significant advantage

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