Job Posting Title:

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Hiring Department:

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Position Open To:

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Weekly Scheduled Hours:

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FLSA Status:

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Earliest Start Date:

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Position Duration:

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Location:

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Job Details:

General Notes

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 focuses on applying and expanding proficiency in MLOps practices. The AI/Machine Engineer II uses practical knowledge of machine learning operations to help build scalable, secure, and reliable ML infrastructure in a healthcare setting.

Responsibilities

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.

Knowledge/Skills/Abilities

Functional/Technical Skills

• 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.

Peer Relationships

• 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.

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 regulated environments.

Licenses, Registrations or Certifications

Required:

• None

Preferred:

• Cloud platform certifications (e.g., foundational or associate-level).
• Introductory certifications in machine learning or DevOps practices.

Salary Range

$89,946 + depending on qualifications

Working Conditions

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

Required Materials

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

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.

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Employment Eligibility:

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Retirement Plan Eligibility:

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Background Checks:

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

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Equal Opportunity Employer:

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.

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Pay Transparency:

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.

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Employment Eligibility Verification:

If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form.  You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States.  Documents need to be presented no later than the third day of employment.  Failure to do so will result in loss of employment at the university.

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E-Verify:

The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:

• E-Verify Poster (English and Spanish) [PDF]
• Right to Work Poster (English) [PDF]
• Right to Work Poster (Spanish) [PDF]

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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 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 all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.