AI Engineer

utaustin; Austin (TX); On-site; USD 81,000 - 99,000; Full time

Job summary

Dell Medical School in Austin, TX seeks an AI Engineer II to support ML model deployment, monitoring, and governance in production. The role emphasizes building scalable MLOps infrastructure within a healthcare setting.

Reporting to the Director of Data Intelligence and Decision Science, you will collaborate with data scientists, software engineers, and DevOps to automate retraining, deployment, and security controls.

Qualifications

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

Responsibilities

• Develops ML deployment pipelines and governance.
• Implements lifecycle governance standards for versioning.
• Optimizes workflows for scalability and reliability.
• Implements monitoring systems for drift and performance.
• Collaborates with data scientists, software engineers, and DevOps.
• Automates retraining and deployment processes.
• Implements data governance and security protocols.
• Configures cloud infrastructure for ML workloads.
• Contributes to technical documentation and knowledge sharing.
• Assists with proof-of-concept evaluations and post-mortems.

Skills

ML programming

ML lifecycle

CI/CD pipelines

Cloud infrastructure

Container orchestration

Problem solving

Cross-functional collaboration

Education

Bachelor's degree in Computer Science/Engineering/Data Science

Minimum 2 years of relevant experience

Tools

CI/CD tools

Kubernetes

Docker

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

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

Standa