Senior MLOps Engineer

EBSCO Information Services

. Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments .

Posted 4/21/2026full-timeRemote • Massachusetts • 🇺🇸 United StatesSenior💰 $120,120 - $171,600 per yearWebsite

Tech Stack

Tools & technologies

AWSDockerETLJenkinsPythonPyTorchScikit-LearnTensorflowTerraform

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

ML Opsmodel trainingmodel validationmodel deploymentautomationinfrastructure-as-codemodel versioningreproducibilitylineage trackingfeature engineering

Soft Skills

communicationcollaborationmentoringknowledge sharingcontinuous improvement

Certifications

Bachelor's Degree in Computer ScienceBachelor's Degree in Data Engineering