OPT Cloud Engineer Jobs
Cloud Engineer jobs are among the most OPT-friendly roles in tech. Employers filing H-1B visa petitions consistently list cloud infrastructure positions in their top sponsored titles, and the specialty occupation standard is well-established for this role. STEM OPT extension eligibility gives you up to three years of authorized work, which meaningfully reduces the risk employers associate with sponsorship timelines.
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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
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Get Access To All JobsTips for Finding OPT Sponsorship as a Cloud Engineer
Target employers with active cloud infrastructure teams
Companies running production workloads on AWS, Azure, or GCP are far more likely to sponsor than those with minimal cloud footprints. H-1B visa LCA disclosure data shows cloud engineer roles concentrated at tech, finance, and enterprise SaaS employers.
Lead with your STEM OPT extension eligibility
Most CS and engineering degrees qualify for the 24-month STEM extension, giving employers three full years of OPT authorization. Frame this early in conversations so hiring managers understand the sponsorship runway before they raise concerns about timing.
Get certified on at least one major cloud platform
AWS Solutions Architect, Google Cloud Professional, or Azure Administrator certifications signal job-ready skills and remove the ambiguity around whether your role qualifies as a specialty occupation. Certified candidates routinely clear recruiter screens faster than non-certified peers.
Emphasize infrastructure-as-code experience in applications
Terraform, Pulumi, and AWS CDK experience directly supports the specialty occupation argument for H-1B filings. Employers and immigration attorneys find it easier to justify sponsorship when the role clearly requires specialized technical knowledge beyond general IT skills.
Apply to mid-size companies alongside large enterprises
Mid-size SaaS companies and cloud-native startups often have faster hiring cycles and less rigid HR gatekeeping around OPT. Many have sponsored H-1Bs before and are open to doing it again for strong candidates with the right cloud stack experience.
Raise OPT timing during the offer stage, not the application stage
Avoid volunteering your visa status in cover letters or early screens. Once an employer is interested and moving toward an offer, walk them through your exact authorization dates and what the H-1B cap-gap covers. Specificity reduces perceived risk significantly.
Cloud Engineer OPT: Frequently Asked Questions
Do Cloud Engineer roles qualify for the STEM OPT extension?
Yes, in almost every case. Cloud Engineer positions are grounded in computer science, electrical engineering, or information technology, all of which appear on the STEM Designated Degree Program list. If your degree is in one of those fields, you can apply for the 24-month extension before your initial 12-month OPT expires, giving you three years of total work authorization.
How common is H-1B sponsorship for Cloud Engineers?
Cloud Engineer is one of the more frequently sponsored roles in the H-1B program. DOL LCA disclosure data consistently shows cloud infrastructure and DevOps titles among top petitioned positions at major tech employers. The specialty occupation standard is well-established because the role requires a specific technical degree and advanced cloud platform knowledge, which makes USCIS approval more predictable than it is for generalist IT titles.
Where can I find Cloud Engineer jobs that are open to OPT candidates?
Migrate Mate filters job listings specifically for visa-sponsoring employers, so you're not wasting applications on companies that won't hire on OPT or won't sponsor H-1B afterward. Browsing Migrate Mate lets you focus on employers with a documented sponsorship history, which is a much more efficient approach than sending cold applications and hoping the recruiter doesn't screen you out at the first mention of your visa status.
Can I work as a Cloud Engineer on OPT while waiting for my H-1B lottery result?
Yes, and the cap-gap rule protects your status during the wait. If your employer files an H-1B petition before your OPT expires, your work authorization automatically extends through September 30 of the fiscal year even if your OPT end date falls earlier. If you're selected in the lottery, your H-1B status begins October 1. If you're not selected, you'll need to leave or find another status, so it's worth understanding your contingency options before the results come out.
Does my specific cloud specialization affect my chances of getting sponsored?
It can matter at the margins. Roles focused on cloud security, platform engineering, and site reliability engineering tend to have stronger specialty occupation arguments because they require narrower and more advanced expertise. General cloud support or cloud administrator roles can face more scrutiny in H-1B adjudication if the job description is broad or lists a bachelor's degree in any field as sufficient. Framing your experience around specific platforms, tools, and technical depth strengthens both your application and any future immigration filing.