OPT Cloud Software Engineer Jobs
Cloud Software Engineer jobs are among the most OPT-friendly roles in tech. Employers routinely sponsor H-1B visas for this title, and your F-1 OPT gives you up to 12 months of work authorization, extendable to 36 months with a STEM OPT extension if your degree qualifies.
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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 526+ OPT Cloud Software Engineer Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Cloud Software Engineer
Confirm your degree qualifies for STEM OPT
Cloud Software Engineer roles almost always qualify for STEM OPT extension, but your degree field must be on the STEM Designated Degree Program List. Computer science, electrical engineering, and information systems typically qualify. Verify your specific CIP code with your DSO before applying.
Target employers with H-1B sponsorship history
Companies that have filed H-1B petitions for Cloud Software Engineers in the past are far more likely to sponsor you after OPT. Large cloud providers, enterprise software firms, and funded startups with engineering teams are your most reliable targets. Research their sponsorship track record before applying.
Earn cloud certifications before you apply
AWS, Google Cloud, and Azure certifications signal technical depth to hiring managers and reduce perceived onboarding risk. Certifications like AWS Solutions Architect or Google Associate Cloud Engineer are widely recognized and meaningfully strengthen applications from OPT candidates without prior U.S. work experience.
Start your STEM OPT extension application early
USCIS recommends filing your STEM OPT extension up to 90 days before your standard OPT expires. Processing can take weeks. Filing late risks a gap in work authorization, which could disrupt your employment. Coordinate with your DSO the moment you accept a job offer.
Highlight cloud-native project work in your application
Employers want evidence you have worked with Kubernetes, Terraform, CI/CD pipelines, or major cloud platforms. Include specific projects from internships, coursework, or personal work. Concrete technical contributions reduce the perceived risk of sponsoring a candidate still on OPT status.
Be transparent about your OPT timeline with recruiters
Recruiters for Cloud Software Engineer roles handle visa sponsorship regularly, but they need accurate timelines to plan. Tell them your OPT expiration date, whether you qualify for STEM extension, and when H-1B sponsorship would be needed. Clarity builds trust and avoids late-stage offer withdrawals.
Cloud Software Engineer OPT: Frequently Asked Questions
Can I work as a Cloud Software Engineer on F-1 OPT?
Yes. Cloud Software Engineer is a specialty occupation that directly aligns with STEM degree programs, making it one of the most straightforward roles for F-1 OPT work authorization. Standard OPT gives you 12 months of employment authorization. If your degree is in a qualifying STEM field, you can apply for a 24-month STEM OPT extension, giving you up to 36 months total.
Do Cloud Software Engineer employers commonly sponsor H-1B visas?
Cloud Software Engineer is one of the most H-1B-sponsored job titles in the U.S. technology sector. Major cloud providers, enterprise software companies, and well-funded startups routinely file H-1B petitions for this role. You can browse Cloud Software Engineer roles from verified sponsoring employers on Migrate Mate, which filters specifically for positions open to OPT and visa sponsorship candidates.
Does a Cloud Software Engineer role qualify for STEM OPT extension?
It almost always does, provided your underlying degree is in a qualifying STEM field such as computer science, software engineering, or computer engineering. The role itself involves software development in cloud infrastructure, which falls squarely within the technical scope USCIS expects for STEM OPT. Confirm your specific degree CIP code with your DSO to be certain before applying.
What cloud skills are most valued by employers sponsoring OPT candidates?
Employers consistently prioritize hands-on experience with at least one major cloud platform (AWS, Google Cloud, or Azure), container orchestration tools like Kubernetes, and infrastructure-as-code tools like Terraform or Pulumi. Familiarity with CI/CD pipelines and distributed systems design also appears frequently in job requirements. Candidates who hold a recognized cloud certification alongside practical project experience tend to move faster through the interview process.
Can I switch employers during my OPT or STEM OPT extension as a Cloud Software Engineer?
Yes, but you must report the change to your DSO within 10 days and update your SEVIS record. During standard OPT, you can change employers freely as long as your new role is directly related to your degree. During STEM OPT extension, the new employer must also be enrolled in E-Verify, and you will need a new Form I-983 training plan signed by the new employer.