STEM OPT Senior Mlops Engineer Jobs
Senior MLOps Engineer roles sit squarely within STEM OPT eligibility, letting you work up to 36 months total while your employer handles E-Verify enrollment. Your STEM degree in computer science, data science, or a related field covers the extension, giving you a full runway to grow into production ML infrastructure at scale.
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Get Access To All JobsINTRODUCTION
EBSCO Information Services (EBSCO) delivers a fully optimized research experience, seamlessly integrated with a powerful discovery platform to support the information needs and maximize the research experience of our end-users. Headquartered in Ipswich, MA, EBSCO employs more than 2,700 people worldwide, with most embracing hybrid or remote work models. As an AI-enabled service leader, we thrive on innovation, forward-thinking strategies, and the dedication of our exceptional team. At EBSCO, we’re driven to inspire, empower and support research. Our mission is to transform lives by providing reliable and relevant information — when, where and how people need it. We’re seeking dynamic, creative individuals whose diverse perspectives will help us achieve this global, inclusive mission. Join us to help make an impact.
Your Opportunity
As a Senior ML Ops Engineer 1, you will play a key role in designing, building, and maintaining production-grade machine learning (ML) pipelines and infrastructure within our AWS-based data lakehouse ecosystem. Working alongside data engineers, data scientists, and DevSecOps teams, you will operationalize ML models and ensure the reliability, security, and scalability of the ML lifecycle—from data ingestion through training, deployment, and monitoring. You will help shape the ML Ops framework, contribute to automation that accelerates delivery, and ensure alignment with established platform Non-Functional Requirements (NFRs). This is a highly collaborative, hands-on engineering role requiring a deep understanding of AWS services, automation, and ML workflow orchestration. This position is remote and operates within a distributed agile environment.
- Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments.
- Implement automation for model packaging, testing, deployment, and monitoring using CI/CD best practices.
- Collaborate with data engineers and data scientists to operationalize ML workloads within the data lakehouse ecosystem.
- Develop and maintain integrations between data ingestion, feature stores, and model repositories.
- Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure.
- Implement and manage model versioning, reproducibility, and lineage tracking using tools such as MLflow or SageMaker Model Registry.
- Define and automate monitoring, alerting, and retraining strategies for deployed models.
- Ensure all ML infrastructure and pipelines meet enterprise security, compliance, and governance standards.
- Participate in code reviews, knowledge sharing, and continuous improvement of ML Ops practices.
- Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams.
Your Team:
This role is part of the Data & AI organization, focusing on the operationalization of ML models and pipelines within AWS. Areas of specialty include:
- ML pipeline automation and orchestration
- Model versioning, governance, and observability
- Feature store integration and reproducibility
- Secure, compliant, and scalable ML infrastructure
- Continuous improvement of ML lifecycle automation
About You
- Bachelor's Degree in Computer Science, Data Engineering, or a related technical field or equivalent experience.
- 4+ years of professional experience in software, data, or ML engineering.
- 2+ years of direct experience implementing and maintaining ML pipelines in production.
- Strong proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Hands-on experience with AWS services (SageMaker, Step Functions, Lambda, ECR, S3, Glue, IAM).
- Solid understanding of CI/CD, containerization (Docker).
- Experience with building CI/CD pipelines (Jenkins, Github Actions, etc.).
- Experience with infrastructure-as-code and automation (Terraform, AWS CDK, or CloudFormation).
- Strong understanding of data pipelines, ETL/ELT concepts, and feature engineering in a lakehouse environment.
- Proven ability to apply software engineering practices to machine learning workflows.
- Strong communication and collaboration skills across multidisciplinary teams.
What sets you apart:
- Experience with feature stores, data catalogs, and metadata management.
- Familiarity with model governance and compliance frameworks.
- Experience with model monitoring and drift detection tools (CloudWatch, or custom solutions).
- Understanding of data lakehouse technologies such as Apache Iceberg or Delta Lake.
- Contributions to open-source ML Ops or DevOps tooling.
- Experience in Agile development environments and cross-functional collaboration.
Pay Range
USD $120,120.00 - USD $171,600.00 /Yr.
The actual salary offer will carefully consider a wide range of factors including your skills, qualifications, education, training, and experience, as well as the position’s work location.
EBSCO provides a generous benefits program including:
- Medical, Dental, Vision, Life and Disability Insurance and Flexible spending accounts
- Retirement Savings Plan
- Paid Parental Leave
- Holidays and Paid Time Off (PTO)
- Mentoring program
And much more! Check it out here: https://www.ebsco.com/about/benefits
We are an equal opportunity employer and comply with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, sex, pregnancy status, age, national origin or ancestry, ethnicity, religion, creed, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment. We comply with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local law.
Not seeing the perfect job?
Join the EBSCO talent community to receive updates on new opportunities that align with your skills and interests - register using the links.
INTRODUCTION
EBSCO Information Services (EBSCO) delivers a fully optimized research experience, seamlessly integrated with a powerful discovery platform to support the information needs and maximize the research experience of our end-users. Headquartered in Ipswich, MA, EBSCO employs more than 2,700 people worldwide, with most embracing hybrid or remote work models. As an AI-enabled service leader, we thrive on innovation, forward-thinking strategies, and the dedication of our exceptional team. At EBSCO, we’re driven to inspire, empower and support research. Our mission is to transform lives by providing reliable and relevant information — when, where and how people need it. We’re seeking dynamic, creative individuals whose diverse perspectives will help us achieve this global, inclusive mission. Join us to help make an impact.
Your Opportunity
As a Senior ML Ops Engineer 1, you will play a key role in designing, building, and maintaining production-grade machine learning (ML) pipelines and infrastructure within our AWS-based data lakehouse ecosystem. Working alongside data engineers, data scientists, and DevSecOps teams, you will operationalize ML models and ensure the reliability, security, and scalability of the ML lifecycle—from data ingestion through training, deployment, and monitoring. You will help shape the ML Ops framework, contribute to automation that accelerates delivery, and ensure alignment with established platform Non-Functional Requirements (NFRs). This is a highly collaborative, hands-on engineering role requiring a deep understanding of AWS services, automation, and ML workflow orchestration. This position is remote and operates within a distributed agile environment.
- Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments.
- Implement automation for model packaging, testing, deployment, and monitoring using CI/CD best practices.
- Collaborate with data engineers and data scientists to operationalize ML workloads within the data lakehouse ecosystem.
- Develop and maintain integrations between data ingestion, feature stores, and model repositories.
- Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure.
- Implement and manage model versioning, reproducibility, and lineage tracking using tools such as MLflow or SageMaker Model Registry.
- Define and automate monitoring, alerting, and retraining strategies for deployed models.
- Ensure all ML infrastructure and pipelines meet enterprise security, compliance, and governance standards.
- Participate in code reviews, knowledge sharing, and continuous improvement of ML Ops practices.
- Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams.
Your Team:
This role is part of the Data & AI organization, focusing on the operationalization of ML models and pipelines within AWS. Areas of specialty include:
- ML pipeline automation and orchestration
- Model versioning, governance, and observability
- Feature store integration and reproducibility
- Secure, compliant, and scalable ML infrastructure
- Continuous improvement of ML lifecycle automation
About You
- Bachelor's Degree in Computer Science, Data Engineering, or a related technical field or equivalent experience.
- 4+ years of professional experience in software, data, or ML engineering.
- 2+ years of direct experience implementing and maintaining ML pipelines in production.
- Strong proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Hands-on experience with AWS services (SageMaker, Step Functions, Lambda, ECR, S3, Glue, IAM).
- Solid understanding of CI/CD, containerization (Docker).
- Experience with building CI/CD pipelines (Jenkins, Github Actions, etc.).
- Experience with infrastructure-as-code and automation (Terraform, AWS CDK, or CloudFormation).
- Strong understanding of data pipelines, ETL/ELT concepts, and feature engineering in a lakehouse environment.
- Proven ability to apply software engineering practices to machine learning workflows.
- Strong communication and collaboration skills across multidisciplinary teams.
What sets you apart:
- Experience with feature stores, data catalogs, and metadata management.
- Familiarity with model governance and compliance frameworks.
- Experience with model monitoring and drift detection tools (CloudWatch, or custom solutions).
- Understanding of data lakehouse technologies such as Apache Iceberg or Delta Lake.
- Contributions to open-source ML Ops or DevOps tooling.
- Experience in Agile development environments and cross-functional collaboration.
Pay Range
USD $120,120.00 - USD $171,600.00 /Yr.
The actual salary offer will carefully consider a wide range of factors including your skills, qualifications, education, training, and experience, as well as the position’s work location.
EBSCO provides a generous benefits program including:
- Medical, Dental, Vision, Life and Disability Insurance and Flexible spending accounts
- Retirement Savings Plan
- Paid Parental Leave
- Holidays and Paid Time Off (PTO)
- Mentoring program
And much more! Check it out here: https://www.ebsco.com/about/benefits
We are an equal opportunity employer and comply with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, sex, pregnancy status, age, national origin or ancestry, ethnicity, religion, creed, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment. We comply with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local law.
Not seeing the perfect job?
Join the EBSCO talent community to receive updates on new opportunities that align with your skills and interests - register using the links.
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Get Access To All JobsTips for Finding STEM OPT Authorization as a Senior Mlops Engineer
Confirm your CIP code covers MLOps
Check that your degree's Classification of Instructional Programs code maps to an approved STEM field before applying. Computer science, software engineering, and data science CIP codes all qualify. Your DSO can confirm eligibility against the official STEM OPT designated degree program list.
Verify E-Verify enrollment before accepting offers
STEM OPT requires your employer to be enrolled in E-Verify, not just registered. Ask recruiters for their E-Verify company ID and cross-check it in the E-Verify employer search tool before signing anything. A company that processes I-9s but isn't actively enrolled cannot sponsor your extension.
Benchmark your offer against prevailing wage
MLOps roles fall under SOC codes tied to software developers or computer and information research scientists. Run your offered title and location through the OFLC Wage Search to confirm the salary meets the wage level required for your experience tier before your employer files.
Target employers with active ML platform teams
Companies running production model pipelines on Kubernetes or cloud-native infrastructure are far more likely to have compliant STEM OPT onboarding processes already in place. Search Migrate Mate to filter Senior MLOps Engineer roles by employers verified for STEM OPT hiring.
Draft your I-983 training plan early in negotiations
The I-983 requires your employer to describe your learning objectives, supervision structure, and how the role ties to your STEM degree. Bring a draft to the offer stage so your manager and HR aren't surprised. Roles centered on CI/CD pipelines, model monitoring, and feature stores map cleanly to STEM training goals.
File your OPT extension well before your initial EAD expires
USCIS recommends filing the STEM OPT extension at least 90 days before your initial OPT end date. A late filing forfeits your cap-gap protection and can create an authorization gap between jobs. Your DSO must update your I-20 with the STEM extension recommendation before USCIS will accept the I-765.
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Find Senior Mlops Engineer JobsFrequently Asked Questions
Does a Senior MLOps Engineer role qualify for the STEM OPT extension?
Yes, provided your degree is in a STEM-designated field such as computer science, software engineering, or data science. The role itself must relate directly to your degree, and your employer must be enrolled in E-Verify. The O*NET profile for MLOps and related machine learning infrastructure roles confirms they fall within STEM occupational classifications that USCIS recognizes for the 24-month extension.
How do I confirm my employer is enrolled in E-Verify?
Ask the recruiter or HR contact for the company's E-Verify employer ID and verify it through the public E-Verify employer search. Enrollment status matters more than intent to enroll. Some companies are registered but not actively using the system, which does not satisfy the STEM OPT requirement. Confirming active participation before your start date prevents authorization problems after you've already accepted the offer.
What goes into the I-983 training plan for an MLOps role?
The I-983 requires your employer to document your learning objectives, supervision frequency, and how each responsibility connects to your STEM degree. For a Senior MLOps Engineer, this typically covers building model deployment pipelines, maintaining feature stores, managing distributed training infrastructure, and implementing observability frameworks. Your manager should sign off on goals that reflect actual engineering work, not generic job description language, to satisfy USCIS review.
What happens to my STEM OPT authorization if my MLOps role changes significantly?
If your job duties shift materially, your employer must update your I-983 within five business days and your DSO must record the change. A role that moves away from machine learning infrastructure into general software development or management without direct STEM application could affect your extension's validity. USCIS can request the updated I-983 during audits, so keeping it current is a compliance requirement, not optional documentation.
Where can I find Senior MLOps Engineer jobs that already support STEM OPT students?
Migrate Mate lists Senior MLOps Engineer roles from employers verified for STEM OPT hiring, so you're not spending time on applications that will stall at the authorization question. Filtering by role and work authorization type surfaces companies with E-Verify enrollment and a track record of supporting F-1 students through the 24-month extension process.
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