DevOps Engineer

Stanford Health Care•Newark, NJ

115d•$79 - $105

About The Position

Stanford Health Care is seeking an MLOps Engineer to develop and maintain infrastructure and processes for production-grade AI, machine learning, and predictive algorithms supporting patient care, medical research, and operational services. The role combines cloud infrastructure, DevOps orchestration, and AI/ML platform operations, including support for large language model training and inference pipelines. The position works with AI researchers and leaders in clinical specialties and operations, reports to the Infrastructure group, and has a dotted-line relationship to the Data Science team. It maintains infrastructure-as-code repositories, infrastructure and deployment pipelines, and security practices, and establishes full software development lifecycle standards for the Data Science team. The description also presents a focus on agentic AI platforms: building infrastructure for multi-agent systems and applications, integrating frameworks and healthcare APIs, developing Python RAG pipelines and scalable microservices, and applying observability and safety guardrails to autonomous workflows. Stanford Health Care expects employees to adopt and execute its C-I-CARE standards for patient- and family-centered interactions. The work is classified as Blood Borne Pathogens Category III (no exposure to blood, body fluids, or tissues) and Category I tasks that are not a condition of employment.

Requirements

Bachelor’s degree or higher in Computer Science, Engineering, or a related field.
Three or more years of directly related experience.
Proven experience as an MLOps Engineer.
Strong knowledge of cloud platforms such as AWS, Azure, or Google Cloud, and infrastructure-as-code tools such as Terraform or CloudFormation.
Proficiency with Docker and container orchestration platforms such as Kubernetes.
Experience with CI/CD tools such as GitLab CI/CD, GitHub Actions, or CircleCI.
Programming skills in Python, Rust, or Go, with experience in scripting and automation.
Familiarity with machine learning frameworks and libraries such as PyTorch, TensorFlow, and scikit-learn.
Deep understanding of DevOps principles, agile methodologies, and the software development lifecycle.
Strong problem-solving and troubleshooting skills, including the ability to analyze and resolve complex technical issues.
Excellent communication and collaboration skills for working effectively in cross-functional teams.

Responsibilities

Design, build, and maintain scalable, robust infrastructure for AI/ML systems, including cloud environments, containerization, and orchestration platforms.
Develop and implement CI/CD pipelines to automate deployment, testing, and monitoring of AI/ML models and applications.
Collaborate with data scientists, data engineers, and software engineers to optimize model training, deployment, and inference pipelines.
Monitor and troubleshoot AI/ML systems to maintain high availability, performance, and reliability.
Maintain and monitor model training and inference pipelines across multi-cloud tenants, particularly for large language models.
Maintain Kubernetes pods, the container registry, virtual machine image library, and model registry.
Monitor infrastructure utilization and costs for model training, inference, and GPU usage.
Implement security, data privacy, and compliance best practices in AI/ML workflows and infrastructure.
Evaluate and integrate tools, technologies, and frameworks to improve MLOps processes.
Mentor and provide technical guidance to junior organization members.
Stay current with developments in MLOps, DevOps, and cloud technologies and share relevant information with the team.
Maintain cloud-based infrastructure-as-code repositories, infrastructure, deployment pipelines, and security practices.
Set full software development lifecycle standards for the Data Science team.
Build infrastructure for agentic AI systems and integrate multi-agent frameworks with healthcare APIs, with observability and safety guardrails.