Machine Learning Operations Engineer

The Machine Learning Operations (MLOps) Engineer is responsible for the full lifecycle management of machine learning models, including design, build, deployment, and maintenance. This role plays an integral part in implementing artificial intelligence solutions across Keck Medicine of USC, partnering with data scientists, engineers, and clinical operations teams to deliver scalable, reliable, and compliant AI solutions. The MLOps Engineer ensures seamless integration, automation, and monitoring of models within production environments, leveraging Dev Ops expertise to advance patient care, operational excellence, and clinical research.

• Design, build, deploy, and maintain machine learning models across production environments.
• Partner with data scientists, engineers, and clinical operations to implement AI solutions.
• Develop and continuously improve MLOps pipelines for monitoring, versioning, and deployment.
• Implement best practices for testing, debugging, and performance monitoring of AI systems.
• Ensure seamless integration, automation, and scaling of AI solutions within existing infrastructure.
• Support predictive modeling, large language models (LLMs), and natural language processing (NLP) initiatives.
• Apply Retrieval‑Augmented Generation (RAG) frameworks with LLMs and articulate their advantages.
• Lead engineering efforts in ML/GenAI model workflows and deployment frameworks.
• Develop AI pipelines for data ingestion, preprocessing, and retrieval to meet technical and business requirements.
• Implement CI/CD pipelines for machine learning models, automating testing and deployment.
• Establish monitoring and logging solutions to track model performance and system health.
• Apply version control systems for ML models and associated code.
• Ensure compliance with healthcare regulations, data protection, and privacy standards.
• Maintain clear and comprehensive documentation of MLOps processes and configurations.

• Bachelor’s degree in computer science, artificial intelligence, informatics, or related field.
• Minimum of 3 years of relevant machine learning engineering experience.
• Experience managing end‑to‑end ML lifecycle.
• Proficiency with automation tools such as Terraform.
• Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes).
• Experience with CI/CD tools (e.g., Git Hub Actions).
• Strong programming skills in Python, R, and SQL.
• Deep understanding of coding, architecture, and deployment processes.
• Strong knowledge of critical performance metrics for ML systems.
• Extensive experience in predictive modeling, LLMs, and NLP.
• Familiarity with healthcare regulations, standards, and EHR systems integration.

• Master’s degree in computer science, engineering, or related field.
• Experience with cloud platforms (AWS, Azure, GCP).
• Background in healthcare data and machine learning use cases.
• Technical writing and documentation experience for AI/ML models and processes.

• None required; certifications in cloud platforms, Dev Ops, or machine learning are a plus.