Senior MLOps Engineer

EBSCO Information Services

Senior ML Ops Engineer designing and maintaining production-grade ML pipelines within AWS ecosystem. Collaborating with cross-functional teams to operationalize machine learning workloads and ensure their reliability.

Posted 7/30/2026full-timeIpswich • Massachusetts • 🇺🇸 United StatesSenior💰 $120,120 - $171,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining ML Ops pipelines, utilizing AWS services and infrastructure-as-code tools to automate processes. Strong proficiency in Python and experience with ML frameworks, alongside a solid understanding of CI/CD practices and data engineering principles.

Highest-signal resume keywords

ML Ops Pipeline DevelopmentAWS Services (SageMaker, Lambda, S3)Python ProgrammingInfrastructure-as-Code (Terraform, CloudFormation)CI/CD Best Practices

ATS Keywords

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Hard Skills

ML Pipeline ImplementationPythonAWS CDKTerraformCI/CDDockerETL/ELT ConceptsFeature EngineeringML Frameworks (PyTorch, TensorFlow, Scikit-learn)Model Versioning

Soft Skills

CommunicationCollaborationMentoring

Tools & Technologies

MLflowSageMaker Model RegistryJenkinsGithub ActionsData Lakehouse

Certifications & Qualifications

Bachelor's Degree in Computer Science or Related Field

Industry Keywords

Machine LearningData EngineeringModel DeploymentAutomationCompliance

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