AI Engineer

University of Texas at Austin; Austin (TX); Presencial; USD 81 000 - 99 000; Tempo integral

Resumo da oferta

The University of Texas at Austin is seeking an AI/Machine Engineer II to support development, deployment, and monitoring of machine learning models in production within a healthcare context. You will work under the Director of Data Intelligence and Decision Science to scale secure ML infrastructure, applying MLOps best practices and collaborating across teams.

Responsibilities include building deployment pipelines, enforcing governance, monitoring model performance, and coordinating with data

Qualificações

• Bachelor’s degree in Computer Science, Data Science, Engineering or related field.
• Minimum 2 years of relevant experience.
• Relevant education and experience may be substituted as appropriate.

Responsabilidades

• Develop and refine ML deployment pipelines and workflows for production-grade model lifecycle governance and management.
• Implement lifecycle governance standards to support versioning and reproducibility.
• Optimize workflows for scalability and reliability.
• Implement and maintain monitoring systems to evaluate model performance, reliability, and drift.
• Detect and report model drift and anomalies.
• Create dashboards and alerts for stakeholders.

Descrição da oferta de emprego

The AI/Machine Engineer II role supports the development, deployment, and monitoring of machine learning models in production environments within a healthcare setting. Reporting to the Director of Data Intelligence and Decision Science, this position applies and expands MLOps practices to deliver scalable, secure, and reliable ML infrastructure.

Location and Salary

• Location: Austin, TX (onsite)
• Salary: USD 89,946 per yearly

Responsibilities

• Develop and refine ML deployment pipelines and workflows for production-grade model lifecycle governance and management.
• Implement lifecycle governance standards to support versioning and reproducibility.
• Optimize workflows for scalability and reliability.
• Implement and maintain monitoring systems to evaluate model performance, reliability, and drift.
• Detect and report model drift and anomalies.
• Create dashboards and alerts for stakeholders.
• Collaborate with data scientists, software engineers, and DevOps teams to convert model requirements into operational solutions.
• Coordinate integration with APIs and application workflows.
• Participate in code reviews and design sessions.
• Design and automate retraining, validation, and deployment processes to keep models accurate and responsive.
• Implement checks prior to deployment.
• Schedule retraining jobs based on defined triggers.
• Apply healthcare data governance and security protocols to ML systems to support regulatory compliance.
• Implement access controls and encryption.
• Support compliance audits and documentation.
• Configure and optimize cloud infrastructure for ML workloads, balancing performance with cost-efficiency.
• Tune resource allocation for cost and performance.
• Monitor infrastructure utilization and scaling needs.
• Contribute to technical documentation, process improvement, and knowledge sharing across teams.
• Maintain technical documentation for ML workflows and keep model documentation and compliance records updated.
• 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.
• Adhere to internal controls and the reporting structure.
• Perform related duties as required.

Required 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.

Preferred Qualifications

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

Technologies

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

Certifications

• Required: None
• Preferred: Cloud platform certifications (foundational or associate-level); introductory certifications in machine learning or DevOps practices.

Working Conditions

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

Required Application Materials

• Resume/CV
• 3 work references with contact information, with at least one reference from a supervisor
• Letter of interest

Background Checks

A criminal history background check will be required for finalist(s) under consideration for this position.

Employment Eligibility

• Regular staff employed in their current position for the last six continuous months are eligible for openings recruited through University-Wide or Open Recruiting, including promotional opportunities and lateral transfers.
• Staff who are promotion/transfer eligible may apply for positions without supervisor approval.

Retirement Plan Eligibility

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.

Employment Eligibility Verification and E-Verify

• If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form.
• You must present acceptable and original documents to prove identity and authorization to work in the United States no later than the third day of employment.
• Failure to present documents will result in loss of employment at the university.
• The University of Texas at Austin uses E-Verify to check work authorization for all new hires effective May 2015 (company ID number: 854197).

Compliance

• 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 as 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.
• The Clery Act requires prospective employees to be notified of the availability of the Annual Security and Fire Safety report.