Senior MLOps Engineer

EBSCO

18h•Remote

About The Position

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. 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.