STEM OPT Cloud Engineer Jobs
Cloud Engineer roles in infrastructure, DevOps, and platform engineering qualify for STEM OPT's 24-month extension when your degree falls under an eligible CIP code in computer science, engineering, or a related STEM field. Your employer must be enrolled in E-Verify before your extension starts, and you'll need a signed I-983 training plan tied to cloud-specific learning objectives.
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Get Access To All JobsINTRODUCTION
We are seeking an ML/AI Data & Cloud Engineer to design, build, and operate the cloud-native components that deliver large-scale, data-intensive AI capabilities.
This role focuses on building scalable data platforms, distributed processing systems, and model-serving infrastructure that enable end-to-end machine learning workflows in production. You will work closely with data scientists and product teams to transform advanced models into reliable, high-performance services.
The ideal candidate combines strong engineering skills with a solid grounding in quantitative methods, and is comfortable leveraging modern AI-assisted development tools to accelerate research, prototyping, and delivery.
Responsibilities
- Design and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems
- Build and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows
- Architect data processing systems for feature engineering, large-scale computation, and model training support
- Develop and operate model-serving infrastructure enabling real-time and batch inference via APIs
- Develop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)
- Implement model validation and monitoring frameworks addressing bias, drift, robustness, and performance
- Tune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers
- Build and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining
- Ensure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)
- 3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development
- Strong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling
- Experience designing and operating cloud-native systems (AWS or equivalent)
- Experience building and maintaining large-scale data pipelines and working with high-volume datasets
- Experience developing APIs or microservices for production systems
- Solid understanding of distributed systems, data processing, and system reliability concepts
- Solid foundation in algorithms, data structures, and computational thinking
PREFERRED QUALIFICATIONS
- Familiarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)
- Exposure to machine learning workflows and the infrastructure required to support training and inference at scale
- Experience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, SageMaker, or equivalents)
- Experience with containerization (Docker/Podman, ECS)
- Experience with data lake architectures and multi-source data ingestion patterns
- Familiarity with MLOps practices, including reproducibility, model versioning, and deployment automation
- Working knowledge of statistics, econometrics, quantitative modeling, and optimization
- Familiarity with cloud cost optimization strategies and performance tuning
WHAT YOU’LL WORK ON
- Scalable cloud data platforms integrating diverse, high-volume datasets
- Foundational predictive models that adapt across markets rather than being rebuilt for each use case
- A cloud-native platform supporting data ingestion, model training, inference, and scalable deployment
- Intelligent systems for ranking, matching, scoring, and explanation of model outputs
- Robust evaluation and monitoring frameworks for model performance and reliability
- API-driven integration of AI capabilities into broader software ecosystems
WHY JOIN US?
You’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. This role offers the opportunity to build foundational platforms that support advanced AI capabilities while shaping architecture, tooling, and best practices in a rapidly evolving space.
INTRODUCTION
We are seeking an ML/AI Data & Cloud Engineer to design, build, and operate the cloud-native components that deliver large-scale, data-intensive AI capabilities.
This role focuses on building scalable data platforms, distributed processing systems, and model-serving infrastructure that enable end-to-end machine learning workflows in production. You will work closely with data scientists and product teams to transform advanced models into reliable, high-performance services.
The ideal candidate combines strong engineering skills with a solid grounding in quantitative methods, and is comfortable leveraging modern AI-assisted development tools to accelerate research, prototyping, and delivery.
Responsibilities
- Design and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems
- Build and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows
- Architect data processing systems for feature engineering, large-scale computation, and model training support
- Develop and operate model-serving infrastructure enabling real-time and batch inference via APIs
- Develop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)
- Implement model validation and monitoring frameworks addressing bias, drift, robustness, and performance
- Tune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers
- Build and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining
- Ensure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)
- 3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development
- Strong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling
- Experience designing and operating cloud-native systems (AWS or equivalent)
- Experience building and maintaining large-scale data pipelines and working with high-volume datasets
- Experience developing APIs or microservices for production systems
- Solid understanding of distributed systems, data processing, and system reliability concepts
- Solid foundation in algorithms, data structures, and computational thinking
PREFERRED QUALIFICATIONS
- Familiarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)
- Exposure to machine learning workflows and the infrastructure required to support training and inference at scale
- Experience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, SageMaker, or equivalents)
- Experience with containerization (Docker/Podman, ECS)
- Experience with data lake architectures and multi-source data ingestion patterns
- Familiarity with MLOps practices, including reproducibility, model versioning, and deployment automation
- Working knowledge of statistics, econometrics, quantitative modeling, and optimization
- Familiarity with cloud cost optimization strategies and performance tuning
WHAT YOU’LL WORK ON
- Scalable cloud data platforms integrating diverse, high-volume datasets
- Foundational predictive models that adapt across markets rather than being rebuilt for each use case
- A cloud-native platform supporting data ingestion, model training, inference, and scalable deployment
- Intelligent systems for ranking, matching, scoring, and explanation of model outputs
- Robust evaluation and monitoring frameworks for model performance and reliability
- API-driven integration of AI capabilities into broader software ecosystems
WHY JOIN US?
You’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. This role offers the opportunity to build foundational platforms that support advanced AI capabilities while shaping architecture, tooling, and best practices in a rapidly evolving space.
See all 344+ STEM OPT Cloud Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Cloud Engineer Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a Cloud Engineer
Verify your CIP code matches cloud engineering
Check your degree's CIP code against the DHS STEM Designated Degree Program List before applying. Degrees in computer science, computer engineering, and information systems typically qualify, but applied computing or IT management programs sometimes fall outside the list.
Confirm E-Verify enrollment before accepting offers
Ask your recruiter for the employer's E-Verify Company ID number and cross-check it through the E-Verify employer search before signing anything. A company that isn't enrolled can't legally employ you on STEM OPT, even if they're otherwise enthusiastic about hiring you.
Align your I-983 goals to cloud certification tracks
When drafting your I-983 training plan, map your learning objectives to specific cloud certifications like AWS Solutions Architect or Google Cloud Professional. USCIS reviewers look for measurable, role-specific goals, not vague references to gaining technical experience.
Target employers with active STEM OPT infrastructure
Use Migrate Mate to filter Cloud Engineer roles by employers with verified E-Verify enrollment and a history of hiring OPT students. This cuts the research time on employer eligibility and surfaces openings at companies already familiar with the I-983 process.
Time your H-1B registration to your OPT end date
If your STEM OPT expires before October 1 of your cap-gap year, the cap-gap rule extends your work authorization automatically once USCIS receives your H-1B visa petition. File as early in the registration window as possible to maximize that buffer.
Use the OFLC Wage Search to negotiate cloud role pay
Look up the prevailing wage for your specific cloud engineering role and metro area using the OFLC Wage Search before your offer call. The DOL wage level your employer files at is public record, and knowing it in advance strengthens your negotiating position.
Frequently Asked Questions
Does my degree qualify me for the STEM OPT extension as a Cloud Engineer?
Your degree qualifies if it appears on the DHS STEM Designated Degree Program List, which covers fields like computer science, computer engineering, electrical engineering, and information science. Cloud Engineering roles are classified under SOC codes tied to software development and systems architecture, so most STEM graduates working in cloud infrastructure meet the degree-to-role alignment requirement. Check your degree's CIP code against the current DHS list to confirm before your DSO submits the I-20 recommendation.
What E-Verify requirement applies to Cloud Engineer employers on STEM OPT?
Your employer must be actively enrolled in E-Verify before your STEM OPT extension start date. Enrollment after the fact doesn't satisfy the requirement. You can verify enrollment status through the E-Verify employer search using the company's legal name or EIN. If a company operates through a staffing agency or professional employer organization, the E-Verify enrollment must belong to the entity that signs your I-983, not a parent or affiliated company.
How do I write an I-983 training plan for a Cloud Engineer role?
Your I-983 must identify specific cloud engineering skills you'll develop, the projects or systems you'll work on, and how your employer will supervise and evaluate your progress. Generic language like 'gaining experience in cloud technologies' won't pass scrutiny. Tie each learning objective to concrete deliverables, such as designing a multi-region deployment pipeline or completing an AWS or Azure architecture project, and include a supervisor name with reporting frequency. USCIS can request the I-983 during an inspection, so specificity matters.
What happens to my STEM OPT if I change Cloud Engineer employers?
You can switch employers during your STEM OPT period, but the new employer must also be enrolled in E-Verify, and you must submit a new I-983 training plan signed by both you and the new employer within ten days of starting. Your DSO needs to update your SEVIS record to reflect the change. You cannot start work with the new employer until the I-983 is submitted, so plan the transition timeline carefully to avoid an unauthorized employment gap.
Where can I find Cloud Engineer jobs that already support STEM OPT hiring?
Migrate Mate surfaces Cloud Engineer roles at employers with confirmed E-Verify enrollment, so you're not spending time vetting companies that can't legally hire you on STEM OPT. Roles on the platform are filtered for the specific requirements your extension depends on, which matters when your job search timeline is tied to an OPT end date and you can't afford to lose weeks on employers who haven't set up the required E-Verify infrastructure.