Machine Learning Engineer

Tucker Parker Smith Group (TPS Group) - MLOps Engineer

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Employment Type: Direct Hire

Salary Range: $152,000 – $175,000 (Exempt)

Schedule: Monday–Friday, 8:00 AM – 5:00 PM

Base pay range
$/yr - $/yr

Position Summary

A leading private university in Los Angeles is seeking a highly skilled Machine Learning Operations (MLOps) Engineer to support enterprise-wide artificial intelligence initiatives within its medical enterprise. Under the direction of Information Services leadership, this role is responsible for the full lifecycle management of machine learning models, including design, development, deployment, monitoring, and optimization in production environments.

The MLOps Engineer will collaborate closely with data scientists, data engineers, Dev Ops teams, and clinical operations stakeholders to deliver scalable, secure, and reliable AI solutions that enhance patient care, improve operational efficiency, and advance clinical research. The ideal candidate brings strong Dev Ops expertise, healthcare domain knowledge, and hands‑on experience deploying production‑grade machine learning and GenAI systems.

• Design, deploy, and maintain production‑ready machine learning models with a focus on scalability, reliability, and real‑time inference.
• Build and maintain end‑to‑end MLOps pipelines for model training, versioning, deployment, monitoring, and lifecycle management.
• Develop scalable machine learning infrastructure using on‑premise or cloud platforms such as AWS, GCP, or Azure.
• Implement CI/CD pipelines to automate testing, validation, and deployment of machine learning models.
• Collaborate with data scientists, data engineers, analytics teams, and Dev Ops teams to operationalize ML and GenAI solutions.
• Engineer and optimize workflows for predictive modeling, large language models (LLMs), NLP, and retrieval‑augmented generation (RAG) frameworks.
• Implement monitoring and logging solutions to track model performance, system health, and anomalies.
• Ensure AI systems meet security, compliance, and data privacy standards applicable to healthcare environments.
• Maintain clear, comprehensive technical documentation for ML models, pipelines, and operational processes.

• Bachelor’s degree in Computer Science, Artificial Intelligence, Informatics, Engineering, or a related field.
• 3+ years of experience in machine learning engineering or MLOps roles.
• Proven experience managing the end‑to‑end machine learning lifecycle in production environments.
• Strong experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Hands‑on experience with infrastructure automation tools, including Terraform.
• Proficiency with CI/CD tools such as Git Hub Actions.
• Advanced programming skills in Python, with working knowledge of R and SQL.
• Deep understanding of software architecture, deployment processes, and performance optimization.
• Extensive experience in predictive modeling, LLMs, and NLP.
• Ability to clearly articulate the benefits and applications of RAG frameworks with LLMs.

• Master’s degree in Computer Science, Engineering, or a closely related field.
• Experience working with healthcare data and machine learning use cases.
• Familiarity with Electronic Health Record (EHR) systems and integrating ML models with clinical systems.
• Strong understanding of healthcare regulations, data security, and privacy standards.

Please submit your resume in Word or PDF format to be considered.

Mid‑Senior level

Full‑time

Information Technology

Hospitals and Health Care

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Medical insurance

Vision insurance

401(k)