AI Engineer

Support production machine learning operations in a healthcare environment by building scalable, secure, and reliable MLOps infrastructure.

• Develop and refine ML deployment pipelines and workflows for production-grade ML lifecycle governance and management
• Implement lifecycle governance standards covering versioning and reproducibility
• Optimize workflows for scalability and reliability
• Build and maintain monitoring systems to assess model performance, reliability, and drift proactively
• Detect and report model drift and anomalies
• Create dashboards and alerts for stakeholders
• Collaborate cross-functionally with data scientists, software engineers, and DevOps teams to translate model needs into operational solutions
• Coordinate integrations with APIs and application workflows
• Participate in code reviews and design sessions
• Design and automate retraining, validation, and deployment processes to maintain accuracy and responsiveness
• Implement pre-deployment checks
• Schedule retraining jobs based on triggers
• Apply healthcare data governance and security protocols for regulatory compliance
• Implement access controls and encryption for ML systems
• Support compliance audits and documentation
• Configure and optimize cloud infrastructure for ML workloads, balancing performance and cost-efficiency
• Tune resource allocation to manage cost and performance
• Monitor infrastructure utilization and scaling needs
• Contribute to technical documentation, process improvement, and knowledge sharing
• Maintain documentation for ML workflows
• Share best practices and lessons learned
• Support proof-of-concept evaluations for new MLOps tools
• Participate in post-mortem reviews for ML pipeline incidents
• Update model documentation and compliance records on a periodic basis
• Adhere to internal controls and reporting structure
• Perform related duties as required

Requirements

• 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

Technologies

• MLOps
• CI/CD
• Cloud infrastructure
• Container orchestration

Preferred Qualifications

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

Working Conditions

• Standard office equipment
• Repetitive use of a keyboard
• May be exposed to occupational hazards such as communicable diseases, blood borne pathogens, ionizing and non-ionizing radiation, hazardous medications, and disoriented or combative patients, or others

Additional Requirements and Screening

• Resume/CV
• 3 work references with contact information (at least one reference from a supervisor)
• Letter of interest
• Background check: a criminal history background check will be required for finalist(s)

Employment Eligibility and Compliance

• Regular staff employed in their current position for the last six continuous months are eligible for openings through University-Wide or Open Recruiting, including promotional opportunities and lateral transfers
• Promotion/transfer eligible staff may apply for positions without supervisor approval
• Retirement plan: Teacher Retirement System of Texas (TRS), subject to at least 20 hours per week and at least 135 days in length
• Upon hire, complete the federal Employment Eligibility Verification I-9 and provide acceptable, original documents to prove identity and work authorization by the third day of employment
• E-Verify: used for work authorization checks effective May 2015; company ID number: 854197
• Employees may be required to report violations of law under Title IX and the Jeanne Clery Act (Clery Act)
• If identified as a Campus Security Authority (Clery Act), the employee will be notified and provided resources for reporting
• Title IX responsibilities are defined and outlined in HOP-3031
• The Clery Act requires prospective employees to be notified of the availability of the Annual Security and Fire Safety report

Location: Austin, TX (onsite)

Compensation: USD 89,946 per yearly

Education level: Bachelor’s degree (or related field)

Experience level: 2+ years relevant experience