AI Engineer at The University of Texas at Austin in Austin, TX

Job description

Job Posting Title: AI Engineer ---- Hiring Department: Dell Medical School ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 40 ---- FLSA Status: Exempt from FLSA ---- Earliest Start Date: Immediately ---- Position Duration: Expected to Continue ---- Location: AUSTIN, TX ---- 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