OPT Mlops Engineer Jobs
MLOps Engineer jobs on OPT sit at the intersection of machine learning and production infrastructure, making them highly attractive to F-1 students with STEM-designated degrees. Most roles qualify for the 24-month STEM OPT extension. Employers in this space regularly file H-1B visa petitions, giving you a clear path beyond your initial OPT period.
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Get Access To All JobsINTRODUCTION
If you're ready to be part of our legacy of hope and innovation, we encourage you to take the first step and explore our current job openings. Your best is waiting to be discovered.
ROLE AND RESPONSIBILITIES
We are seeking a high-caliber Senior AI Platform & ML Ops Engineer to architect the "layered" infrastructure required for autonomous, agentic systems within Stanford Healthcare. In this role, you will be the "Master Chef" of our AI ecosystem, seamlessly folding Expert-Level DevOps (Kubernetes, Terraform, DevOps orchestration) with Agentic Application Development (LangGraph, CrewAI, Tool-calling logic). You won't just manage servers; you will build the robust, full-stack "factory" where multi-agent frameworks interact with healthcare APIs, ensuring every autonomous action is governed by strict ML Ops observability (LangSmith, Arize) and safety guardrails. If you have the "crispy" coding skills to build RAG pipelines in Python and the "rich" architectural depth to deploy scalable microservices, extensive full stack software development expertise, we want you to lead the integration of reasoning-based AI into the future of clinical and business workflow automations.
The MLOPs Engineer will play an integral role incorporating Artificial Intelligence (AI) within Stanford Health Care. The solutions will impact patient care, medical research, and operational services. This group is tasked to innovate, build, deploy and monitor production grade AI, machine learning (ML) and predictive algorithms into healthcare. The role will partner closely with lead researchers within the AI field and leaders across various clinical specialties and operations.
This role will report to the Infrastructure group and have a dotted line relationship to the Data Science team. The role will be responsible for maintaining cloud-based infrastructure as code repositories, maintaining infrastructure, deployment pipelines and designing the security landscape for the team and objects. The role will set the standards for the full SDLC of projects for the Data Science team.
LOCATION
Stanford Health Care
What you will do
- Design, build and maintain scalable and robust infrastructure for AI/ML systems, including cloud-based environments, containerization and orchestration platforms.
- Develop and implement CI/CD pipelines to automate the 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 ensure high availability, performance and reliability.
- Maintain and monitor model training and inference pipelines across multi-cloud tenants especially around Large Language Models (LLMs).
- Maintain Kubernetes pods, container registry and virtual machine image library and model registry.
- Monitor infrastructure utilization and costs pertaining to model training, inference and GPU utilization.
- Implement best practices for security, data privacy and compliance in AI/ML workflows and infrastructure.
- Evaluate and integrate new tools, technologies and frameworks to improve the efficiency and effectiveness of our MLOps processes.
- Mentor and provide technical guidance to junior members of the organization.
- Stay up-to-date with the latest advancements and trends in MLOps, DevOps and cloud technologies and share them with the team.
EDUCATION QUALIFICATIONS
Bachelor’s or higher degree in Computer Science, Engineering or a related field
EXPERIENCE QUALIFICATIONS
Three (3) or more years of directly related experience
REQUIRED KNOWLEDGE, SKILLS AND ABILITIES
- Proven experience as an MLOps Engineer.
- Strong knowledge of cloud platforms such as AWS, Azure or Google Cloud and experience with infrastructure-as-code tools like Terraform or CloudFormation.
- Proficiency in containerization technologies such as Docker and container orchestration platforms like Kubernetes.
- Experience with CI/CD tools such as GitLab CI/CD, Github Actions or CircleCI.
- Solid programming skills in languages such as Python, Rust or Go and 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 software development lifecycle.
- Strong problem-solving and troubleshooting skills, with the ability to analyze and resolve complex technical issues.
- Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams.
PHYSICAL DEMANDS AND WORK CONDITIONS
Blood Borne Pathogens
Category III - Tasks that involve NO exposure to blood, body fluids or tissues, and Category I tasks that are not a condition of employment
These principles apply to ALL employees:
Stanford Health Care sets a high standard for delivering value and an exceptional experience for our patients and families. Candidates for employment and existing employees must adopt and execute C-I-CARE standards for all of patients, families and towards each other. C-I-CARE is the foundation of Stanford’s patient-experience and represents a framework for patient-centered interactions. Simply put, we do what it takes to enable and empower patients and families to focus on health, healing and recovery.
You will do this by executing against our three experience pillars, from the patient and family’s perspective:
- Know Me: Anticipate my needs and status to deliver effective care
- Show Me the Way: Guide and prompt my actions to arrive at better outcomes and better health
- Coordinate for Me: Own the complexity of my care through coordination
Equal Opportunity Employer
Stanford Health Care (SHC) strongly values diversity and is committed to equal opportunity and non-discrimination in all of its policies and practices, including the area of employment. Accordingly, SHC does not discriminate against any person on the basis of race, color, sex, sexual orientation or gender identity and/or expression, religion, age, national or ethnic origin, political beliefs, marital status, medical condition, genetic information, veteran status, or disability, or the perception of any of the above. People of all genders, members of all racial and ethnic groups, people with disabilities, and veterans are encouraged to apply. Qualified applicants with criminal convictions will be considered after an individualized assessment of the conviction and the job requirements.
COMPENSATION
Base Pay Scale: Generally starting at $79.21 - $104.97 per hour
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training. This pay scale is not a promise of a particular wage.
INTRODUCTION
If you're ready to be part of our legacy of hope and innovation, we encourage you to take the first step and explore our current job openings. Your best is waiting to be discovered.
ROLE AND RESPONSIBILITIES
We are seeking a high-caliber Senior AI Platform & ML Ops Engineer to architect the "layered" infrastructure required for autonomous, agentic systems within Stanford Healthcare. In this role, you will be the "Master Chef" of our AI ecosystem, seamlessly folding Expert-Level DevOps (Kubernetes, Terraform, DevOps orchestration) with Agentic Application Development (LangGraph, CrewAI, Tool-calling logic). You won't just manage servers; you will build the robust, full-stack "factory" where multi-agent frameworks interact with healthcare APIs, ensuring every autonomous action is governed by strict ML Ops observability (LangSmith, Arize) and safety guardrails. If you have the "crispy" coding skills to build RAG pipelines in Python and the "rich" architectural depth to deploy scalable microservices, extensive full stack software development expertise, we want you to lead the integration of reasoning-based AI into the future of clinical and business workflow automations.
The MLOPs Engineer will play an integral role incorporating Artificial Intelligence (AI) within Stanford Health Care. The solutions will impact patient care, medical research, and operational services. This group is tasked to innovate, build, deploy and monitor production grade AI, machine learning (ML) and predictive algorithms into healthcare. The role will partner closely with lead researchers within the AI field and leaders across various clinical specialties and operations.
This role will report to the Infrastructure group and have a dotted line relationship to the Data Science team. The role will be responsible for maintaining cloud-based infrastructure as code repositories, maintaining infrastructure, deployment pipelines and designing the security landscape for the team and objects. The role will set the standards for the full SDLC of projects for the Data Science team.
LOCATION
Stanford Health Care
What you will do
- Design, build and maintain scalable and robust infrastructure for AI/ML systems, including cloud-based environments, containerization and orchestration platforms.
- Develop and implement CI/CD pipelines to automate the 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 ensure high availability, performance and reliability.
- Maintain and monitor model training and inference pipelines across multi-cloud tenants especially around Large Language Models (LLMs).
- Maintain Kubernetes pods, container registry and virtual machine image library and model registry.
- Monitor infrastructure utilization and costs pertaining to model training, inference and GPU utilization.
- Implement best practices for security, data privacy and compliance in AI/ML workflows and infrastructure.
- Evaluate and integrate new tools, technologies and frameworks to improve the efficiency and effectiveness of our MLOps processes.
- Mentor and provide technical guidance to junior members of the organization.
- Stay up-to-date with the latest advancements and trends in MLOps, DevOps and cloud technologies and share them with the team.
EDUCATION QUALIFICATIONS
Bachelor’s or higher degree in Computer Science, Engineering or a related field
EXPERIENCE QUALIFICATIONS
Three (3) or more years of directly related experience
REQUIRED KNOWLEDGE, SKILLS AND ABILITIES
- Proven experience as an MLOps Engineer.
- Strong knowledge of cloud platforms such as AWS, Azure or Google Cloud and experience with infrastructure-as-code tools like Terraform or CloudFormation.
- Proficiency in containerization technologies such as Docker and container orchestration platforms like Kubernetes.
- Experience with CI/CD tools such as GitLab CI/CD, Github Actions or CircleCI.
- Solid programming skills in languages such as Python, Rust or Go and 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 software development lifecycle.
- Strong problem-solving and troubleshooting skills, with the ability to analyze and resolve complex technical issues.
- Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams.
PHYSICAL DEMANDS AND WORK CONDITIONS
Blood Borne Pathogens
Category III - Tasks that involve NO exposure to blood, body fluids or tissues, and Category I tasks that are not a condition of employment
These principles apply to ALL employees:
Stanford Health Care sets a high standard for delivering value and an exceptional experience for our patients and families. Candidates for employment and existing employees must adopt and execute C-I-CARE standards for all of patients, families and towards each other. C-I-CARE is the foundation of Stanford’s patient-experience and represents a framework for patient-centered interactions. Simply put, we do what it takes to enable and empower patients and families to focus on health, healing and recovery.
You will do this by executing against our three experience pillars, from the patient and family’s perspective:
- Know Me: Anticipate my needs and status to deliver effective care
- Show Me the Way: Guide and prompt my actions to arrive at better outcomes and better health
- Coordinate for Me: Own the complexity of my care through coordination
Equal Opportunity Employer
Stanford Health Care (SHC) strongly values diversity and is committed to equal opportunity and non-discrimination in all of its policies and practices, including the area of employment. Accordingly, SHC does not discriminate against any person on the basis of race, color, sex, sexual orientation or gender identity and/or expression, religion, age, national or ethnic origin, political beliefs, marital status, medical condition, genetic information, veteran status, or disability, or the perception of any of the above. People of all genders, members of all racial and ethnic groups, people with disabilities, and veterans are encouraged to apply. Qualified applicants with criminal convictions will be considered after an individualized assessment of the conviction and the job requirements.
COMPENSATION
Base Pay Scale: Generally starting at $79.21 - $104.97 per hour
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training. This pay scale is not a promise of a particular wage.
See all OPT Mlops Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT Mlops Engineer Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as a Mlops Engineer
Target STEM OPT-eligible roles explicitly
MLOps Engineering consistently falls under STEM-designated CIP codes in computer science and engineering. Confirm your degree qualifies before applying, then filter for employers with active E-Verify enrollment, which is required for the 24-month STEM extension.
Emphasize production ML experience over research
Employers sponsoring OPT want engineers who can ship models to production, not just build notebooks. Highlight experience with model serving, monitoring pipelines, and CI/CD for ML workflows. Research background alone raises questions about job-readiness for engineering teams.
Know your OPT authorization timeline before interviewing
Hiring managers often ask when you can start and how long you can work without sponsorship. Know your OPT end date, your STEM extension eligibility window, and your I-20 program end date so you can answer confidently without hesitation.
Prioritize companies with established MLOps infrastructure
Companies already running Kubeflow, MLflow, or Vertex AI pipelines have proven engineering needs for this role. These organizations are far more likely to sponsor because the business case for the hire is already established and headcount is budgeted.
Address sponsorship directly but briefly in applications
Do not hide OPT status or bury it in cover letters. State clearly that you are authorized to work on OPT and eligible for the STEM extension. Ambiguity causes recruiters to pass. Clarity keeps you in the pipeline.
Build a portfolio around real deployment artifacts
GitHub repos showing deployed model pipelines, monitoring dashboards, or containerized inference services carry more weight than academic projects. Sponsors need evidence you can operate at production scale, not just demonstrate theoretical knowledge of MLOps concepts.
Mlops Engineer OPT: Frequently Asked Questions
Do MLOps Engineer roles qualify for the 24-month STEM OPT extension?
Most do. MLOps Engineering typically falls under STEM-designated fields such as computer science, computer engineering, or data science. Your degree's CIP code determines eligibility, not the job title itself. Confirm your program's CIP code with your DSO before accepting a role, and verify the employer is enrolled in E-Verify, which is a separate requirement for the extension.
How competitive is OPT sponsorship for MLOps Engineer positions specifically?
MLOps is a specialized discipline with a genuine talent shortage, which improves your position relative to more saturated roles like general software engineering. Companies running machine learning in production, particularly in fintech, healthtech, and enterprise SaaS, actively recruit for this skill set. Migrate Mate lists MLOps roles from employers who have demonstrated willingness to work with OPT candidates and file H-1B visa petitions.
What happens to my OPT authorization if my MLOps role is reclassified or my employer is acquired?
Your OPT authorization is tied to your employment in a role directly related to your degree field, not to a specific employer. If your role changes significantly or your employer undergoes a material change such as an acquisition, notify your DSO promptly. A material change that affects your employment terms may require updating your I-20 or filing an amended H-1B if you have already transitioned to that status.
Can I work as a contractor or through a staffing agency as an MLOps Engineer on OPT?
Contract and staffing arrangements are permitted on OPT, but they carry more scrutiny than direct employment. The work must still be directly related to your degree and meet the minimum 20-hour-per-week requirement. Self-employment is also allowed under OPT rules, though it requires careful documentation. For any arrangement, keep records of your employer of record and the scope of work performed.
What technical skills make an MLOps candidate more likely to receive sponsorship?
Employers prioritizing sponsorship want candidates with demonstrable production experience, specifically containerization with Docker and Kubernetes, experiment tracking tools like MLflow or Weights and Biases, and cloud ML platforms such as AWS SageMaker, GCP Vertex AI, or Azure ML. Candidates who can also write infrastructure-as-code using Terraform or Pulumi stand out. These skills signal that you can operate independently in a production environment from day one.