OPT Cloud Sales Engineer Jobs
Cloud Sales Engineer jobs are actively sponsored for OPT, with employers filing H-1B visa petitions well before your 12-month authorization window closes. Roles sit at the intersection of technical depth and revenue generation, making them attractive to companies who see long-term value in sponsoring international talent.
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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 Sales Engineer Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Cloud Sales Engineer
Target companies with a history of H-1B filings
Cloud vendors and managed service providers file hundreds of H-1B visa petitions annually. Focus your search on employers with consistent sponsorship records, as they already have internal processes for transitioning OPT hires to long-term visa status.
Earn a cloud certification before applying
AWS Solutions Architect, Google Cloud Professional, or Azure credentials signal credibility to hiring managers. Certified candidates close more enterprise deals and are easier for employers to justify sponsoring through the H-1B process.
Position yourself at the technical end of the sales spectrum
Employers sponsor roles requiring specialized knowledge, not generalist sales skills. Emphasize your ability to architect solutions, run proof-of-concept projects, and translate infrastructure requirements into business value during every interview.
Start the OPT clock conversation early
Bring up your authorization timeline during the offer stage, not after. Employers familiar with sponsorship appreciate transparency, and it lets HR confirm the H-1B filing schedule before your first day.
Apply to STEM OPT-eligible roles if your degree qualifies
A computer science, engineering, or information systems degree may qualify you for a 24-month STEM OPT extension, giving employers three years total to sponsor your H-1B, well past the annual lottery deadline.
Tailor your resume to enterprise and cloud-native accounts
Highlight any experience with AWS, Azure, or GCP environments, even academic or internship exposure. Employers hiring for enterprise cloud sales want evidence you can speak credibly to IT architects and procurement leads.
Cloud Sales Engineer OPT: Frequently Asked Questions
Do Cloud Sales Engineer roles qualify for STEM OPT extension?
It depends on your degree, not the job title. If your bachelor's or master's is in a STEM-designated field such as computer science, information systems, or electrical engineering, you likely qualify for the 24-month extension. The role itself is not evaluated for STEM eligibility, but most Cloud Sales Engineers hold technical degrees that do qualify.
How do I find Cloud Sales Engineer jobs that sponsor OPT on Migrate Mate?
Migrate Mate filters job listings by visa sponsorship type, so you can browse Cloud Sales Engineer roles posted by employers who have explicitly indicated they sponsor OPT or H-1B candidates. This removes the guesswork of cold-applying to companies that may not have a sponsorship process in place.
Can I work as a Cloud Sales Engineer on OPT without prior full-time experience?
Yes, though most employers prefer candidates who have completed internships or capstone projects involving cloud platforms. Demonstrating hands-on exposure to AWS, Azure, or GCP through coursework, certifications, or internships is often sufficient for entry-level roles. Your OPT authorization is valid regardless of experience level as long as the role is related to your field of study.
Is a Cloud Sales Engineer role considered a specialty occupation for H-1B purposes?
Generally yes. Cloud Sales Engineer positions typically require a bachelor's degree or higher in computer science, engineering, or a related technical field, which meets USCIS specialty occupation standards. Roles requiring only general business skills without a specific degree requirement may face more scrutiny, so the technical nature of the job description matters significantly.
What happens to my OPT authorization if I move from one Cloud Sales Engineer role to another?
You can change employers during OPT as long as the new role is directly related to your field of study. You must report the change to your DSO within ten days and update your record in SEVIS. There is no gap restriction between roles, but working without a qualifying job or in an unrelated field puts your status at risk.