OPT Operations Engineer Jobs
Operations Engineer jobs are a strong fit for F-1 OPT students with degrees in industrial engineering, systems engineering, or operations management. Most roles qualify as STEM OPT extensions, giving you up to 36 months of work authorization. Employers in manufacturing, logistics, and tech actively hire OPT candidates for process improvement and systems optimization work.
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Get Access To All JobsINTRODUCTION
The AI & ML Operations Engineer designs, builds, and maintains the engineering processes and platform capabilities needed to deploy, monitor, govern, and maintain deployed AI/ML solutions at scale. The role converts validated analytical work into reliable production solutions and manages the operational lifecycle of models and related services while meeting enterprise privacy, security, and support standards.
The engineer partners with data scientists, data engineers, central cloud and infrastructure teams, API and Interoperability teams, Cybersecurity, service owners, and relevant engineering teams to deliver maintainable solutions. The role remains hands-on in engineering, testing, deployment, and troubleshooting while using enterprise-managed capabilities for production data movement, integration, and infrastructure. Scientific method validation remains with the Data Scientist and designated reviewers; this position implements the operational controls for deployed solutions.
This role is part of a new AI & Automation engineering team and will directly design, build, deploy, and support production AI applications and services. Work may include agents, retrieval-augmented generation, orchestration, APIs and integrations, automated evaluation and testing frameworks, observability, monitoring, release and rollback processes, and model/application lifecycle management.
Responsibilities
Model deployment and orchestration. Deploy batch and real-time models and related AI services using approved CI/CD, containers, APIs, and workflow patterns. Build and maintain assigned model deployment and runtime components and coordinate dependencies with enterprise platform owners.
Release management. Automate packaging, versioning, testing, promotion, rollback, and recovery for models and services. Implement retraining or recalibration workflows using criteria defined with data scientists and the required validation and release approvals.
ML platform capabilities. Implement, integrate, configure, and operate approved feature stores, model registries, experiment tracking, and related ML lifecycle capabilities with enterprise platform owners. Partner with Cloud & AI Deployment Engineering and central cloud teams on compute, storage, networking, and deployment.
Deployed-model governance. Implement approved lifecycle controls, access restrictions, lineage, audit trails, change records, and release approvals for deployed models. Maintain traceability across data, code, model, and configuration versions.
Monitoring and reliability. Implement logging, alerts, and monitoring for model performance, drift, data quality, latency, availability, and errors. Work with data scientists, service owners, and relevant engineering teams on measures, thresholds, incident response, and service objectives.
Data and API integration. Work with central data-engineering teams on managed data pipelines and with enterprise API and Interoperability teams on real-time and application integrations, including MuleSoft or other approved API capabilities. Coordinate monitoring, recovery checkpoints, restart procedures, security, and support ownership.
Enterprise support and maintainability. Ensure production solutions have documented dependencies, operational procedures, escalation paths, and ongoing ownership before release. Arrange support with the responsible enterprise teams, including 24/7 coverage where required for critical services, rather than relying on an individual project owner.
AI application operations. Operate assigned LLM, retrieval-based, and agent applications using automated quality checks, model/tool tracing, prompt and configuration versioning, and usage/cost monitoring within the common production lifecycle.
Security and engineering standards. Apply enterprise privacy, security, encryption, role-based access, and sensitive-data controls. Follow common infrastructure-as-code, pull-request, and deployment practices with the responsible teams; bring needed tooling or design exceptions through established review processes.
Collaboration and improvement. Help scientists and partner teams move prototypes into supported production solutions. Review code, tests, and documentation, resolve incidents across dependencies, provide constructive technical feedback, and improve reliability, reproducibility, performance, and efficiency.
BASIC QUALIFICATIONS
- B.S./B.A. in Computer Science, Engineering, Information Systems, or a related field, or an equivalent combination of education and relevant experience.
- At least three years of experience in software engineering, DevOps, or MLOps, including work with production systems.
- Proficiency in Python or another modern programming language, such as Java or Go, sufficient to build, test, troubleshoot, and maintain production components.
- Hands-on experience deploying and operating batch and/or real-time machine-learning models in production.
- Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps; source control, code review, automated testing, and controlled releases.
- Hands-on experience with containerization and orchestration technologies using enterprise-approved platforms and tools.
- Experience in cloud and/or hybrid environments such as AWS, Azure, or GCP.
- Working knowledge of SQL and experience with large datasets and data-workflow orchestration; ability to integrate model services with enterprise-managed pipelines and APIs.
- Experience implementing logging, metrics, monitoring, alerts, and operational diagnostics and investigating failures across model, application, data, and infrastructure dependencies.
- Understanding of security best practices, role-based access control, secrets management, and handling sensitive data.
- Ability to independently deliver assigned engineering work, communicate technical tradeoffs, and collaborate with data scientists, clinicians, data/API teams, cloud/DevOps, security, and support functions.
PREFERRED QUALIFICATIONS
- Experience in healthcare, health technology, or another regulated industry handling PHI/PII.
- Familiarity with EHR/EMR integration and healthcare data standards such as HL7/FHIR, ICD, CPT, LOINC, or SNOMED.
- Experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML, including model registries, feature stores, and experiment tracking.
- Experience with ETL/ELT, data warehouse or lakehouse platforms, enterprise API platforms, infrastructure as code, and formal production-support practices.
- Experience monitoring model drift, fairness, and explainability and implementing healthcare AI governance controls.
- Experience operating LLM, retrieval-based, or agent applications, including quality evaluation, tracing, prompt/configuration management, and cost monitoring.
COMPENSATION
- $109,449.60 - $154,523.20 Annual
INTRODUCTION
The AI & ML Operations Engineer designs, builds, and maintains the engineering processes and platform capabilities needed to deploy, monitor, govern, and maintain deployed AI/ML solutions at scale. The role converts validated analytical work into reliable production solutions and manages the operational lifecycle of models and related services while meeting enterprise privacy, security, and support standards.
The engineer partners with data scientists, data engineers, central cloud and infrastructure teams, API and Interoperability teams, Cybersecurity, service owners, and relevant engineering teams to deliver maintainable solutions. The role remains hands-on in engineering, testing, deployment, and troubleshooting while using enterprise-managed capabilities for production data movement, integration, and infrastructure. Scientific method validation remains with the Data Scientist and designated reviewers; this position implements the operational controls for deployed solutions.
This role is part of a new AI & Automation engineering team and will directly design, build, deploy, and support production AI applications and services. Work may include agents, retrieval-augmented generation, orchestration, APIs and integrations, automated evaluation and testing frameworks, observability, monitoring, release and rollback processes, and model/application lifecycle management.
Responsibilities
Model deployment and orchestration. Deploy batch and real-time models and related AI services using approved CI/CD, containers, APIs, and workflow patterns. Build and maintain assigned model deployment and runtime components and coordinate dependencies with enterprise platform owners.
Release management. Automate packaging, versioning, testing, promotion, rollback, and recovery for models and services. Implement retraining or recalibration workflows using criteria defined with data scientists and the required validation and release approvals.
ML platform capabilities. Implement, integrate, configure, and operate approved feature stores, model registries, experiment tracking, and related ML lifecycle capabilities with enterprise platform owners. Partner with Cloud & AI Deployment Engineering and central cloud teams on compute, storage, networking, and deployment.
Deployed-model governance. Implement approved lifecycle controls, access restrictions, lineage, audit trails, change records, and release approvals for deployed models. Maintain traceability across data, code, model, and configuration versions.
Monitoring and reliability. Implement logging, alerts, and monitoring for model performance, drift, data quality, latency, availability, and errors. Work with data scientists, service owners, and relevant engineering teams on measures, thresholds, incident response, and service objectives.
Data and API integration. Work with central data-engineering teams on managed data pipelines and with enterprise API and Interoperability teams on real-time and application integrations, including MuleSoft or other approved API capabilities. Coordinate monitoring, recovery checkpoints, restart procedures, security, and support ownership.
Enterprise support and maintainability. Ensure production solutions have documented dependencies, operational procedures, escalation paths, and ongoing ownership before release. Arrange support with the responsible enterprise teams, including 24/7 coverage where required for critical services, rather than relying on an individual project owner.
AI application operations. Operate assigned LLM, retrieval-based, and agent applications using automated quality checks, model/tool tracing, prompt and configuration versioning, and usage/cost monitoring within the common production lifecycle.
Security and engineering standards. Apply enterprise privacy, security, encryption, role-based access, and sensitive-data controls. Follow common infrastructure-as-code, pull-request, and deployment practices with the responsible teams; bring needed tooling or design exceptions through established review processes.
Collaboration and improvement. Help scientists and partner teams move prototypes into supported production solutions. Review code, tests, and documentation, resolve incidents across dependencies, provide constructive technical feedback, and improve reliability, reproducibility, performance, and efficiency.
BASIC QUALIFICATIONS
- B.S./B.A. in Computer Science, Engineering, Information Systems, or a related field, or an equivalent combination of education and relevant experience.
- At least three years of experience in software engineering, DevOps, or MLOps, including work with production systems.
- Proficiency in Python or another modern programming language, such as Java or Go, sufficient to build, test, troubleshoot, and maintain production components.
- Hands-on experience deploying and operating batch and/or real-time machine-learning models in production.
- Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps; source control, code review, automated testing, and controlled releases.
- Hands-on experience with containerization and orchestration technologies using enterprise-approved platforms and tools.
- Experience in cloud and/or hybrid environments such as AWS, Azure, or GCP.
- Working knowledge of SQL and experience with large datasets and data-workflow orchestration; ability to integrate model services with enterprise-managed pipelines and APIs.
- Experience implementing logging, metrics, monitoring, alerts, and operational diagnostics and investigating failures across model, application, data, and infrastructure dependencies.
- Understanding of security best practices, role-based access control, secrets management, and handling sensitive data.
- Ability to independently deliver assigned engineering work, communicate technical tradeoffs, and collaborate with data scientists, clinicians, data/API teams, cloud/DevOps, security, and support functions.
PREFERRED QUALIFICATIONS
- Experience in healthcare, health technology, or another regulated industry handling PHI/PII.
- Familiarity with EHR/EMR integration and healthcare data standards such as HL7/FHIR, ICD, CPT, LOINC, or SNOMED.
- Experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML, including model registries, feature stores, and experiment tracking.
- Experience with ETL/ELT, data warehouse or lakehouse platforms, enterprise API platforms, infrastructure as code, and formal production-support practices.
- Experience monitoring model drift, fairness, and explainability and implementing healthcare AI governance controls.
- Experience operating LLM, retrieval-based, or agent applications, including quality evaluation, tracing, prompt/configuration management, and cost monitoring.
COMPENSATION
- $109,449.60 - $154,523.20 Annual
See all 1,072+ OPT Operations Engineer Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as an Operations Engineer
Confirm your role qualifies for STEM OPT
Operations Engineer positions typically fall under CIP codes for industrial or systems engineering, qualifying for the 24-month STEM OPT extension. Verify your degree field matches before applying so you can confidently discuss your full authorization timeline with employers.
Lead with your authorization window upfront
Tell recruiters you have 12 months of OPT with a 24-month STEM extension available. Framing your total authorized period as up to 36 months reduces concern about short-term availability and positions you as a longer-term hiring option worth sponsoring.
Target employers with existing E-Verify enrollment
STEM OPT extensions require your employer to be enrolled in E-Verify. Prioritize companies that already use it, particularly large manufacturers, logistics firms, and tech companies, so your extension isn't delayed by administrative enrollment requirements on the employer side.
Quantify process improvements in your resume
Operations Engineer roles are output-driven. Hiring managers respond to measurable results like reduced cycle times, cost savings, or throughput gains. Specific numbers from internships or academic projects demonstrate the analytical mindset employers expect and make sponsorship conversations easier to justify internally.
Pursue lean and Six Sigma certifications early
Lean Six Sigma Green Belt or similar credentials signal operational competency beyond your degree. These certifications are recognized across manufacturing, supply chain, and tech operations teams, making you a stronger candidate in competitive applicant pools where OPT status may otherwise raise hesitation.
Prepare a clear answer about your long-term visa path
Employers sponsoring OPT workers often think ahead to H-1B visa. Know whether your role qualifies as a specialty occupation and be ready to explain the H-1B timeline. Showing you understand the process reduces perceived risk and signals you are serious about building a career in the U.S.
Operations Engineer OPT: Frequently Asked Questions
Does an Operations Engineer role qualify for the STEM OPT extension?
Most Operations Engineer positions qualify for the 24-month STEM OPT extension if your degree is in industrial engineering, systems engineering, mechanical engineering, or a related STEM field. The key is that your CIP code must appear on the official STEM Designated Degree Program List published by ICE. Confirm with your DSO before applying.
How do I find Operations Engineer jobs that sponsor OPT students?
Migrate Mate is built specifically for F-1 OPT students and filters jobs by sponsorship willingness, so you are not sorting through listings from employers who won't work with OPT candidates. Operations Engineer roles in manufacturing, logistics, and tech operations tend to appear frequently because those industries rely heavily on engineering talent and often have structured immigration support.
Can I work as an Operations Engineer while on OPT if my degree is in a related but not identical field?
OPT requires your job to be directly related to your major area of study, but it does not require an exact match in job title. A degree in mechanical engineering, supply chain management, or industrial engineering can support an Operations Engineer role if the core responsibilities align with your field. Your DSO makes the final determination, so discuss the role description with them before accepting an offer.
What happens to my OPT status if my Operations Engineer employer goes through layoffs?
If you are laid off while on post-completion OPT, you have a 90-day unemployment allowance across your entire OPT period. STEM OPT adds up to 60 additional days of unemployment. You need to report the job loss to your DSO immediately and begin your search. Exceeding the unemployment limit without a new job offer ends your authorized stay.
Do Operations Engineer roles at large companies offer H-1B sponsorship after OPT?
Many large employers in manufacturing, tech, and logistics do sponsor H-1B visas for Operations Engineers because the role generally qualifies as a specialty occupation requiring at least a bachelor's degree in a specific field. However, H-1B approval depends on the lottery, so timing your OPT period around the April filing window matters. Ask about sponsorship policy during the offer stage, not after you start.