AI Engineer

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The AI/Machine Engineer II supports the development, deployment, and monitoring of machine learning models in production environments. Reporting to the Director of Data Intelligence and Decision Science, this career-development role focuses on applying and expanding proficiency in MLOps practices. The AI/Machine Engineer II uses practical knowledge of machine learning operations to help build scalable, secure, and reliable ML infrastructure in a healthcare setting.

Machine Learning Deployment and Governance

• Develops and refines ML deployment pipelines and workflows to support production-grade machine learning lifecycle governance and management.
• Implements lifecycle governance standards for versioning and reproducibility.
• Optimizes workflows for scalability and reliability.

Model Monitoring and Performance

• Implements and maintains monitoring systems to proactively assess model performance, reliability, and drift.
• Detects and reports model drift and anomalies.
• Generates dashboards and alerts for stakeholders.

Cross-Functional Collaboration

• Collaborates with data scientists, software engineers, and Dev Ops teams to translate model requirements into operational solutions.
• Coordinates integration with APIs and application workflows.
• Participates in code reviews and design sessions.

Automation of Retraining and Deployment

• Designs and automates retraining, validation, and deployment processes to ensure model accuracy and responsiveness.
• Implements checks before deployment.
• Schedules retraining jobs based on triggers.

Data Governance and Security

• Applies healthcare data governance and security protocols to ML systems, ensuring compliance with regulatory standards.
• Implements access controls and encryption.
• Supports compliance audits and documentation.

Cloud Infrastructure Optimization

• Configures and optimizes cloud infrastructure for ML workloads, balancing performance and cost-efficiency.
• Tunes resource allocation for cost and performance.
• Monitors infrastructure utilization and scaling needs.

Documentation and Knowledge Sharing

• Contributes to technical documentation, process improvement, and knowledge sharing across teams.
• Maintains technical documentation for ML workflows.
• Shares best practices and lessons learned.

Marginal or Periodic Functions

• Assists with proof-of-concept evaluations for new MLOps tools.
• Participates in post-mortem reviews for ML pipeline incidents.
• Supports periodic updates to model documentation and compliance records.
• Adheres to internal controls and reporting structure.
• Performs related duties as required.

Knowledge/Skills/Abilities

Functional/Technical Skills

• Demonstrates proficiency in programming and scripting for ML workflows and automation.
• Applies knowledge of machine learning concepts, model lifecycle, and production deployment.
• Possesses a working knowledge of CI/CD pipelines, cloud infrastructure, and container orchestration.

Problem Solving

• Troubleshoots and optimizes ML systems in dynamic, real-world environments.
• Exhibits strong analytical and problem-solving skills for identifying and resolving operational challenges.
• Initiates process improvements and adopts emerging MLOps practices.

Adaptability and Continuous Learning

• Commits to continuous learning and professional development in machine learning operations.
• Adapts quickly to new tools and emerging best practices.
• Prototypes with unfamiliar libraries and seeks feedback.

Process Management

• Organizes ML workflows for efficiency and reliability.
• Automates retraining and…