AI Engineer

The University of Texas at Austin’s Dell Medical School is hiring an AI Engineer to support the development, deployment, and monitoring of machine learning models in production. The role focuses on applying MLOps practices to build scalable, secure, and reliable ML infrastructure in a healthcare setting.

Responsibilities

• 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
• 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
• Collaborates with data scientists, software engineers, and DevOps teams to translate model requirements into operational solutions
• Coordinates integration with APIs and application workflows
• Participates in code reviews and design sessions
• Designs and automates retraining, validation, and deployment processes to ensure model accuracy and responsiveness
• Implements checks before deployment
• Schedules retraining jobs based on triggers
• 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
• 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
• Contributes to technical documentation, process improvement, and knowledge sharing across teams
• Maintains technical documentation for ML workflows
• Shares best practices and lessons learned
• 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

Preferred

• 3+ years of experience in MLOps, DevOps, or ML engineering
• Experience in healthcare or regulated environments
• Cloud platform certifications (e.g., foundational or associate-level)
• Introductory certifications in machine learning or DevOps practices
• 3+ years of experience in MLOps, DevOps, or ML engineering.
• MLOps
• DevOps
• Machine Learning Engineering
• Healthcare Industry Experience
• Regulated Environment Experience
• Cloud Platform Certification
• Machine Learning Certification
• DevOps Practices Certification

Benefits

• Eligible for the Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.

Company

• Public university offering higher education and research.
• Austin, Texas, USA
• http://www.texasenterprise.utexas.edu/
• Founded in 1883
• 10001+ employees

Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field
• Minimum 2 years of relevant experience
• Relevant education and experience may be substituted as appropriate
• 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
• 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
• 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
• Organizes ML workflows for efficiency and reliability
• Automates retraining and evaluation jobs
• Standardizes packaging and environments for reproducibility
• Handles unclear requirements through testing and iteration
• Communicates trade-offs clearly and revisits decisions as new data emerges
• Designs small experiments to reduce uncertainty
• Communicates and collaborates effectively across technical and non-technical teams
• Participates in reviews and stand-ups to share updates
• Co-develops APIs and integration points
• 3+ years of experience in MLOps, DevOps, or ML engineering
• Experience in healthcare or regulated environments
• Cloud platform certifications (e.g., foundational or associate-level)
• Introductory certifications in machine learning or DevOps practices
• Minimum 2 years of relevant experience.

Required skills

• MLOps, Machine Learning, CI/CD, Cloud Infrastructure, DevOps, Healthcare, Production ML Deployment

Preferred skills

• 3+ years of experience in MLOps, DevOps, or ML engineering, Experience in healthcare or regulated environments

Experience

• 2+ years