STEM OPT Cloud Operations Engineer Jobs
Cloud Operations Engineer roles in infrastructure management, site reliability, and cloud platform support qualify for STEM OPT when your degree is in computer science, information technology, or a related STEM field. The 24-month STEM OPT extension requires your employer to be enrolled in E-Verify and to co-sign your I-983 training plan before your initial OPT expires.
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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 342+ STEM OPT Cloud Operations Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Cloud Operations Engineer Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a Cloud Operations Engineer
Verify your CIP code before applying
Cloud Operations Engineer roles typically align with CIP codes in computer science, computer engineering, or information systems. Confirm your degree's CIP code with your DSO before your initial OPT ends to make sure your extension application is filed on the correct basis.
Filter job postings by E-Verify status
STEM OPT requires your employer to be enrolled in E-Verify before your extension is approved. Ask recruiters directly whether the hiring entity is E-Verify enrolled, since parent companies and subsidiaries are registered separately and the job posting rarely specifies which legal entity you'd join.
Use Migrate Mate to target employers with cloud hiring history
Search Migrate Mate for Cloud Operations Engineer roles filtered by employers who have sponsored STEM OPT students before. Seeing which companies have actively filed training plans in your field saves you from applying to roles where sponsorship is unlikely to move forward.
Benchmark your offer against DOL prevailing wage
Your I-983 training plan must reflect bona fide employment at a wage comparable to similarly situated U.S. workers. Pull the prevailing wage for cloud operations roles in your metro area using the OFLC Wage Search to check whether your offer clears that threshold before you sign.
Negotiate your start date around your 90-day filing window
You must file your STEM OPT extension application with USCIS up to 90 days before your initial OPT expires. If your job offer lands inside that window, align your start date so the I-765 is filed before expiration and your 180-day cap-gap period covers any gap between authorization dates.
Prepare your I-983 training plan before the offer stage
Many employers slow down STEM OPT onboarding because they haven't seen an I-983 before. Draft a completed template showing cloud operations learning objectives tied to your STEM degree field so your hiring manager and HR team can co-sign quickly without delaying your start date.
Frequently Asked Questions
Does a Cloud Operations Engineer role qualify for the STEM OPT extension?
Yes, if your underlying degree is in a STEM-designated field such as computer science, computer engineering, information technology, or systems engineering. The role itself doesn't need to carry a specific title, but your I-983 training plan must connect the practical training objectives to your STEM degree field. Your DSO confirms eligibility based on your degree's CIP code, not your job title.
What does E-Verify enrollment mean for my Cloud Operations Engineer job search?
STEM OPT regulations require your employer to be enrolled in E-Verify before USCIS approves your extension. Enrollment is at the employer entity level, so a large technology company may have some subsidiaries or business units enrolled and others not. Ask HR to confirm the specific legal entity that will employ you is actively enrolled in E-Verify before you accept an offer.
What goes into the I-983 training plan for a Cloud Operations Engineer role?
Your I-983 must describe the specific skills, knowledge, and cloud operations competencies you'll develop and explain how they connect to your STEM degree. For this role, that typically includes infrastructure automation, incident response workflows, cloud platform administration, and monitoring tooling. Your employer co-signs the form and must conduct a self-evaluation at 12 months and a final evaluation at the end of your extension period.
How does cap-gap protection work if my STEM OPT extension is pending when initial OPT expires?
If you file a timely STEM OPT extension application before your initial OPT EAD expires, cap-gap protection automatically extends your work authorization for up to 180 days while USCIS adjudicates. You can continue working as a Cloud Operations Engineer during this period. Your I-94 reflects the cap-gap extension, and your employer's E-Verify case should be run against that updated record.
Where can I find Cloud Operations Engineer jobs where employers already understand STEM OPT?
Migrate Mate lets you browse Cloud Operations Engineer roles filtered for employers with a track record of hiring STEM OPT students. Working with employers who have gone through the I-983 and E-Verify process before shortens onboarding time and lowers the risk that HR delays or unfamiliarity with the extension process will affect your start date or authorization.