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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** The University of Texas - **Location:** Austin, TX, United States - **Experience:** Experienced - **Salary:** $89,946.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Code Review, Computer Programming, Data Governance, DevOps, Monitoring of Systems, Machine Learning, Software Deployment, Management of Software Versions, Scripting, Cloud Platform System, Delivery Pipeline, Kubernetes, Information Technology, Machine Learning Operations - **Published:** October 15, 2026 - **Apply:** https://utaustin.wd1.myworkdayjobs.com/UTstaff/job/AUSTIN-TX/AI-Engineer_R_00049413 ## About the Role * 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 evaluation jobs. * Standardizes packaging and environments for reproducibility. Dealing with Ambiguity * Handles unclear requirements through testing and iteration. * Communicates trade-offs clearly and revisits decisions as new data emerges. * Designs small experiments to reduce uncertainty., * 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., * 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., A criminal history background check will be required for finalist(s) under consideration for this position., * E-Verify Poster (English and Spanish) [PDF] * Right to Work Poster (English) [PDF] * Right to Work Poster (Spanish) [PDF] ## Description 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 DevOps 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., * 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., The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length., Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.