Summary of Duties and Responsibilities:
Looking for an opportunity to apply AI and machine learning in ways that truly matter?
If so, the Translational AI Center (TrAC) at Iowa State University is seeking applicants for a MLOps (Machine Learning Operations) Engineer! This position will be classified as a Research and Development Engineer I.
What You'll Do:
Build and maintain machine learning infrastructure, CI/CD pipelines, and automated data workflows.
Implement model tracking, versioning, and reproducibility practices.
Deploy and manage machine learning models in cloud and production environments.
Containerize applications and support scalable deployment platforms.
Develop and maintain APIs, applications, and services that deliver AI solutions.
Monitor system performance, troubleshoot issues, and optimize reliability.
Write, test, and maintain high-quality, reusable software.
Create and maintain technical documentation.
Participate in code reviews and contribute to shared tools and libraries.
Collaborate with researchers and stakeholders to transition prototypes into production systems.
Support system integration and communicate technical requirements.
Provide technical guidance and training as needed.
Preferred Qualifications:
Bachelor's degree or above in Computer Science, Computer Engineering, Software Engineering, Data Science, or a related technical field.
Two years of related experience in backend software development, AI/ML model deployment, MLOps, DevOps, cloud/platform engineering, or a closely related technical area.
Experience deploying AI/ML models from prototype to production, including containerization, model versioning, automated deployment, monitoring, and operational support.
Strong backend development experience, preferably in Python, including APIs, distributed services, databases, testing, and reusable software components.
Experience with CI/CD, Git-based workflows, containerization, infrastructure automation, experiment tracking, observability, and secure deployment practices.
Experience with cloud platforms such as AWS, Azure, or Google Cloud, including compute, storage, identity management, monitoring, and managed AI/ML services.
Experience deploying and supporting shared technical platforms, including user access management, upgrades, backups, capacity planning, and user support.