AI Engineer

The University of Texas at Austin • Austin, TX

26d

About The Position

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 applies and expands proficiency in MLOps practices, helping build scalable, secure, and reliable ML infrastructure in a healthcare setting.

The role develops and refines ML deployment pipelines and lifecycle governance; implements systems to monitor model performance, reliability, and drift; collaborates across data science, software engineering, and DevOps teams; and automates model retraining, validation, and deployment. It also applies healthcare data governance and security protocols, optimizes cloud infrastructure, and contributes to technical documentation and knowledge sharing.

The position may also assist with MLOps tool proof-of-concept evaluations, participate in post-mortem reviews of ML pipeline incidents, support updates to model documentation and compliance records, adhere to internal controls and reporting structure, and perform related duties as required. The position is based in Austin, Texas, with 40 scheduled hours per week. The expected duration is ongoing, and the earliest start date is immediately.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field; relevant education and experience may be substituted as appropriate.
• Minimum 2 years of relevant experience.
• Proficiency in programming and scripting for ML workflows and automation.
• Knowledge of machine learning concepts, model lifecycle, and production deployment.
• Working knowledge of CI/CD pipelines, cloud infrastructure, and container orchestration.
• Strong analytical and problem-solving skills, including troubleshooting and optimizing ML systems in dynamic, real-world environments.
• Ability to organize and automate ML workflows, retraining, and evaluation jobs and standardize packaging and environments for reproducibility.
• Ability to handle unclear requirements through testing and iteration, communicate trade-offs, and design experiments to reduce uncertainty.
• Effective communication and collaboration across technical and non-technical teams.

Nice To Haves

• 3+ years of experience in MLOps, DevOps, or ML engineering.
• Experience in healthcare or regulated environments.
• Cloud platform certifications, such as foundational or associate-level.
• Introductory certifications in machine learning or DevOps practices.

Responsibilities

• Develop and refine ML deployment pipelines and workflows for production-grade lifecycle governance and management.
• Implement versioning and reproducibility standards and optimize workflows for scalability and reliability.
• Implement and maintain systems that monitor model performance, reliability, and drift; detect and report drift and anomalies; and generate stakeholder dashboards and alerts.
• Collaborate with data scientists, software engineers, and DevOps teams to operationalize model requirements and coordinate integrations with APIs and application workflows.
• Participate in code reviews and design sessions.
• Design and automate model retraining, validation, and deployment processes; implement pre-deployment checks and schedule retraining jobs based on triggers.
• Apply healthcare data governance and security protocols, including access controls and encryption, and support compliance audits and documentation.
• Configure and optimize cloud infrastructure for ML workloads, balancing performance and cost; tune resources and monitor utilization and scaling needs.
• Maintain technical documentation for ML workflows and share best practices and lessons learned.
• Assist with proof-of-concept evaluations for new MLOps tools.
• Participate in post-mortem reviews for ML pipeline incidents and support periodic updates to model documentation and compliance records.
• Adhere to internal controls and reporting structure and perform related duties as required.

Benefits

• Teacher Retirement System of Texas (TRS) retirement plan eligibility, subject to the position meeting the stated hours-per-week and duration requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level