AI Engineer

The University of Texas at Austin; United States; On-site; USD 81,000 - 99,000; Full time

Job summary

The University of Texas at Austin is seeking an AI Engineer to join our ML deployment and governance team. You will design and automate scalable ML pipelines, ensure versioning and reproducibility, and monitor model performance and drift in a healthcare-focused environment.

Collaborating with data scientists, software engineers, and DevOps, you will implement checks before deployment and optimize cloud resources for cost-efficiency. A strong foundation in ML operations is required.

Qualifications

• Bachelor's degree in CS/DS/Engineering or related field.
• Minimum 2 years of relevant experience.
• Educational/experience substitutions may apply.
• 3+ years in MLOps, DevOps, or ML engineering preferred.
• Experience in healthcare or regulated environments preferred.

Responsibilities

• Develops and refines ML deployment pipelines and workflows.
• Implements lifecycle governance standards for versioning and reproducibility.
• Optimizes workflows for scalability and reliability.
• Implements monitoring for model performance, drift, and reliability.
• Generates dashboards and alerts for stakeholders.
• Collaborates with data scientists, software engineers, and DevOps teams.
• Automates retraining and deployment processes.
• Applies healthcare data governance and security protocols.

Skills

Programming for ML workflows

ML concepts & production deployment

CI/CD pipelines

Cloud infrastructure

Container orchestration

Education

Bachelor's degree in Computer Science or related field

Minimum 2 years of relevant experience

Tools

CI/CD pipelines

Cloud infrastructure

Container orchestration

Job description

AI Engineer

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.

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

Tagged as: Life Sciences