OPT Cloud Operations Engineer Jobs
Cloud Operations Engineer jobs are a strong fit for F-1 OPT students with backgrounds in computer science, information systems, or related fields. Most roles qualify as STEM OPT extensions, giving you up to 36 months of work authorization. Demand is consistent across cloud-heavy industries including finance, healthcare, and enterprise software.
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Get Access To All JobsINTRODUCTION
The Cloud & AI Deployment Engineer designs, implements, and supports assigned Azure cloud infrastructure and deployment capabilities for AI and machine-learning solutions in partnership with central Cloud Engineering and infrastructure owners.
The role combines hands-on cloud engineering with collaboration across DevOps, Security, and AI/ML teams to deliver reliable, secure environments that can be supported and maintained long term. The engineer follows enterprise infrastructure-as-code, pull-request, deployment, and cloud-governance practices, including FinOps tagging.
The role identifies gaps and helps establish AI-specific cloud guardrails, reusable patterns, and implementation guidelines with the responsible enterprise teams. The position supports cloud and infrastructure delivery; MLOps retains deployed-model operations, and data scientists and designated reviewers retain scientific method validation.
This role will serve as the AI & Automation team’s dedicated cloud engineering resource, enabling AI engineers through secure, repeatable Azure environments and deployment patterns while working alongside existing enterprise and contracted cloud resources. The role may also support AWS and hybrid-cloud needs where applicable.
Responsibilities
Azure cloud implementation. Design, implement, configure, and maintain assigned Azure infrastructure supporting AI/ML solutions in partnership with central Cloud Engineering and infrastructure teams. Coordinate compute, storage, networking, access, and dependencies using approved cloud capabilities.
Infrastructure as code and deployment. Build and maintain version-controlled infrastructure and deployment configurations. Use pull requests, peer review, testing, and enterprise CI/CD processes for repeatable deployment, promotion, rollback, and recovery. Align GitHub and DevOps practices with designated enterprise owners.
AI cloud guardrails and standards. Identify implementation gaps and help develop and apply approved AI cloud guidelines, reusable patterns, and guardrails with central Cloud Engineering, Architecture, and Security. Ensure deployments follow shared standards rather than isolated, project-specific practices.
Cloud governance and FinOps. Apply enterprise-required resource tagging and cloud-governance practices to support ownership, cost visibility, and efficient resource use. Work with the responsible enterprise teams to address tagging and governance gaps and maintain standards across cloud implementations.
Security and access. Implement approved encryption, access-control, and sensitive-data protections for cloud resources. Coordinate security requirements with security and infrastructure owners and bring needed tooling or design exceptions through established review processes.
Production readiness and support. Document configurations, dependencies, monitoring, logging, operational procedures, recovery steps, and escalation paths with responsible service owners before production use. Coordinate ongoing support, including the coverage required for critical services, rather than relying on an individual project owner.
Reliability and troubleshooting. Monitor and troubleshoot assigned cloud infrastructure and deployment processes. Work with MLOps and enterprise service owners to investigate failures, resolve infrastructure dependencies, and maintain availability and performance.
Cross-team delivery and ownership. Partner with central cloud, infrastructure, DevOps, data/API, and AI/ML teams to deliver supported environments and deployment capabilities. Clarify implementation and support responsibilities and contribute to updating responsibility assignments as enterprise platform ownership evolves.
Documentation and continuous improvement. Maintain technical documentation, reusable deployment patterns, and support information. Provide constructive technical feedback, share implementation guidance, and improve reproducibility, efficiency, and long-term support and maintainability across AI cloud deployments.
BASIC QUALIFICATIONS
- B.S./B.A. in Computer Science, Engineering, Information Technology, Information Systems, or a related field; or a combination of education and experience equivalent to a bachelor’s degree.
- Three years of experience in cloud engineering, DevOps, platform engineering, infrastructure automation, or related work, including demonstrated hands-on experience with Microsoft Azure.
- Demonstrated hands-on experience with Azure cloud engineering and deployment in an enterprise environment.
- Experience implementing and maintaining infrastructure as code and reusable deployment configurations.
- Experience with source control, pull requests, CI/CD, and controlled deployment processes using GitHub, Azure DevOps, or comparable enterprise-managed tools.
- Ability to implement and troubleshoot cloud compute, storage, networking, and access configurations in partnership with infrastructure teams.
- Experience applying cloud governance, resource tagging, and enterprise deployment and support standards.
- Understanding of security best practices, role-based access control, encryption, and sensitive-data handling in cloud environments.
- Experience with monitoring, logging, operational troubleshooting, and documented production support for cloud-hosted services.
- Ability to identify gaps and develop clear technical guidelines, guardrails, and reusable implementation patterns with enterprise owners.
- Ability to independently deliver assigned work, communicate technical tradeoffs, and collaborate with central cloud, infrastructure, DevOps, data/API, Security, and AI/ML teams.
PREFERRED QUALIFICATIONS
- Experience supporting AI/ML deployments or AI applications in Azure.
- Experience in healthcare, health technology, or another regulated industry handling sensitive data.
- Experience with containers and deployment patterns for cloud-hosted applications and services.
- Experience with AWS or hybrid-cloud environments and applying common standards across cloud platforms.
COMPENSATION
- $109,449.60 - $154,523.20 Annual
INTRODUCTION
The Cloud & AI Deployment Engineer designs, implements, and supports assigned Azure cloud infrastructure and deployment capabilities for AI and machine-learning solutions in partnership with central Cloud Engineering and infrastructure owners.
The role combines hands-on cloud engineering with collaboration across DevOps, Security, and AI/ML teams to deliver reliable, secure environments that can be supported and maintained long term. The engineer follows enterprise infrastructure-as-code, pull-request, deployment, and cloud-governance practices, including FinOps tagging.
The role identifies gaps and helps establish AI-specific cloud guardrails, reusable patterns, and implementation guidelines with the responsible enterprise teams. The position supports cloud and infrastructure delivery; MLOps retains deployed-model operations, and data scientists and designated reviewers retain scientific method validation.
This role will serve as the AI & Automation team’s dedicated cloud engineering resource, enabling AI engineers through secure, repeatable Azure environments and deployment patterns while working alongside existing enterprise and contracted cloud resources. The role may also support AWS and hybrid-cloud needs where applicable.
Responsibilities
Azure cloud implementation. Design, implement, configure, and maintain assigned Azure infrastructure supporting AI/ML solutions in partnership with central Cloud Engineering and infrastructure teams. Coordinate compute, storage, networking, access, and dependencies using approved cloud capabilities.
Infrastructure as code and deployment. Build and maintain version-controlled infrastructure and deployment configurations. Use pull requests, peer review, testing, and enterprise CI/CD processes for repeatable deployment, promotion, rollback, and recovery. Align GitHub and DevOps practices with designated enterprise owners.
AI cloud guardrails and standards. Identify implementation gaps and help develop and apply approved AI cloud guidelines, reusable patterns, and guardrails with central Cloud Engineering, Architecture, and Security. Ensure deployments follow shared standards rather than isolated, project-specific practices.
Cloud governance and FinOps. Apply enterprise-required resource tagging and cloud-governance practices to support ownership, cost visibility, and efficient resource use. Work with the responsible enterprise teams to address tagging and governance gaps and maintain standards across cloud implementations.
Security and access. Implement approved encryption, access-control, and sensitive-data protections for cloud resources. Coordinate security requirements with security and infrastructure owners and bring needed tooling or design exceptions through established review processes.
Production readiness and support. Document configurations, dependencies, monitoring, logging, operational procedures, recovery steps, and escalation paths with responsible service owners before production use. Coordinate ongoing support, including the coverage required for critical services, rather than relying on an individual project owner.
Reliability and troubleshooting. Monitor and troubleshoot assigned cloud infrastructure and deployment processes. Work with MLOps and enterprise service owners to investigate failures, resolve infrastructure dependencies, and maintain availability and performance.
Cross-team delivery and ownership. Partner with central cloud, infrastructure, DevOps, data/API, and AI/ML teams to deliver supported environments and deployment capabilities. Clarify implementation and support responsibilities and contribute to updating responsibility assignments as enterprise platform ownership evolves.
Documentation and continuous improvement. Maintain technical documentation, reusable deployment patterns, and support information. Provide constructive technical feedback, share implementation guidance, and improve reproducibility, efficiency, and long-term support and maintainability across AI cloud deployments.
BASIC QUALIFICATIONS
- B.S./B.A. in Computer Science, Engineering, Information Technology, Information Systems, or a related field; or a combination of education and experience equivalent to a bachelor’s degree.
- Three years of experience in cloud engineering, DevOps, platform engineering, infrastructure automation, or related work, including demonstrated hands-on experience with Microsoft Azure.
- Demonstrated hands-on experience with Azure cloud engineering and deployment in an enterprise environment.
- Experience implementing and maintaining infrastructure as code and reusable deployment configurations.
- Experience with source control, pull requests, CI/CD, and controlled deployment processes using GitHub, Azure DevOps, or comparable enterprise-managed tools.
- Ability to implement and troubleshoot cloud compute, storage, networking, and access configurations in partnership with infrastructure teams.
- Experience applying cloud governance, resource tagging, and enterprise deployment and support standards.
- Understanding of security best practices, role-based access control, encryption, and sensitive-data handling in cloud environments.
- Experience with monitoring, logging, operational troubleshooting, and documented production support for cloud-hosted services.
- Ability to identify gaps and develop clear technical guidelines, guardrails, and reusable implementation patterns with enterprise owners.
- Ability to independently deliver assigned work, communicate technical tradeoffs, and collaborate with central cloud, infrastructure, DevOps, data/API, Security, and AI/ML teams.
PREFERRED QUALIFICATIONS
- Experience supporting AI/ML deployments or AI applications in Azure.
- Experience in healthcare, health technology, or another regulated industry handling sensitive data.
- Experience with containers and deployment patterns for cloud-hosted applications and services.
- Experience with AWS or hybrid-cloud environments and applying common standards across cloud platforms.
COMPENSATION
- $109,449.60 - $154,523.20 Annual
See all 525+ OPT Cloud Operations Engineer Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Cloud Operations Engineer
Confirm your role qualifies for STEM OPT
Cloud Operations Engineer positions typically fall under CIP codes for computer science or information technology. Verify your degree matches before applying so you can confidently pursue the 24-month STEM OPT extension without surprises at onboarding.
Target employers with active cloud infrastructure teams
Companies running large-scale AWS, Azure, or GCP environments hire cloud ops engineers regularly and are familiar with OPT authorization. These teams have established onboarding for international hires and are less likely to be confused by your work authorization status.
Get your certifications before job searching
AWS Solutions Architect, Google Associate Cloud Engineer, or Azure Administrator credentials signal job-readiness to hiring managers. Certified candidates on OPT close faster because employers see lower risk. One relevant certification can offset visa sponsorship hesitation significantly.
Be upfront about your OPT timeline and STEM extension
Tell recruiters your current OPT end date and that you qualify for a 24-month STEM extension. Many hiring managers assume OPT means 12 months total. Correcting that assumption early prevents unnecessary rejections based on timeline misunderstanding.
Emphasize hands-on cloud experience in your application
List specific tools, platforms, and incident response experience in your resume. Cloud ops roles are skills-driven. Employers willing to work with OPT students want proof you can contribute immediately, not after months of onboarding and training.
Focus on companies already sponsoring H-1B visas
Employers who regularly file H-1B visa petitions understand long-term visa pathways and are more comfortable hiring on OPT as a first step. This signals they view international hiring as normal, not exceptional, which makes the OPT conversation much easier.
Cloud Operations Engineer OPT: Frequently Asked Questions
Can I work as a Cloud Operations Engineer on F-1 OPT?
Yes. Cloud Operations Engineer roles align directly with computer science and information technology degree programs, which makes them eligible for standard 12-month OPT and, in most cases, the 24-month STEM OPT extension. Your degree field and CIP code determine STEM eligibility, so confirm both with your designated school official before applying.
Does a Cloud Operations Engineer role qualify for the STEM OPT extension?
Most do. Cloud Operations Engineer positions are typically classified under SOC codes tied to computer and information systems occupations, and the underlying degree programs are almost always on the STEM OPT eligible list. Your employer must also be enrolled in E-Verify to approve the extension, so check that requirement early in the hiring process.
How do I find Cloud Operations Engineer jobs that are open to OPT candidates?
Migrate Mate filters job listings specifically for OPT-eligible candidates, so you can search Cloud Operations Engineer roles without sorting through employers who won't consider international students. Most general job boards don't filter by work authorization type, which wastes significant time during an already tight OPT window.
What should I know about the 60-day grace period if I'm between cloud ops jobs?
If your Cloud Operations Engineer role ends before your OPT period expires, you have a 60-day grace period to find new employment. You cannot work during this window, but you can interview and accept offers. If you don't secure a new position within 60 days, your F-1 status terminates, so prioritize your search immediately after your last day.
Do Cloud Operations Engineers typically get H-1B sponsorship after OPT?
Many do, particularly at mid-size and large technology companies where cloud infrastructure is core to the business. Cloud ops is a technical, specialized role that generally satisfies H-1B specialty occupation requirements. The path is most reliable at employers who already file H-1B petitions regularly, since they have established legal and HR processes for the transition.