AI Engineer

AI Engineer

Dell Medical School

All Applicants

40

Exempt from FLSA

Immediately

Expected to Continue

AUSTIN, TX

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.

• 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 Dev Ops 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.

• 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.

• 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.

• 3+ years of experience in MLOps, Dev Ops, or ML…