Senior MLOps Engineer

Role: Senior MLOps Engineer

As a Senior ML Ops Engineer, you will design, build, and maintain production-grade machine learning (ML) pipelines and infrastructure within EBSCO’s AWS-based data lakehouse ecosystem.

Responsibilities

• Build and maintain ML Ops pipelines for model training, validation, and deployment across AWS environments.
• Operationalize ML models end-to-end: data ingestion → training → deployment → monitoring.
• Implement CI/CD automation for model packaging, testing, deployment, and monitoring.
• Collaborate with data engineers and data scientists to run ML workloads within the data lakehouse ecosystem.
• Integrate data ingestion, feature stores, and model repositories.
• Use Infrastructure as Code to automate ML pipeline infrastructure with Terraform, AWS CDK, and/or CloudFormation.
• Ensure model versioning, reproducibility, and lineage tracking using tools such as MLflow or Amazon SageMaker Model Registry.
• Define and automate monitoring, alerting, and retraining strategies for deployed models.
• Ensure ML infrastructure meets enterprise security, compliance, and governance standards.
• Participate in code reviews, knowledge sharing, documentation, and continuous improvement of ML Ops practices.
• Mentor junior engineers and help drive ML Ops standards and best practices across teams.

Requirements

• 4+ years professional experience in software, data, or ML engineering.
• 2+ years direct experience implementing and maintaining ML pipelines in production.
• Python proficiency.
• Familiarity with ML frameworks: PyTorch, TensorFlow, and/or scikit-learn.
• Hands-on AWS experience: SageMaker, Step Functions, Lambda, ECR, S3, Glue, IAM.
• CI/CD and containerization (Docker) experience.
• CI/CD tooling experience (e.g., Jenkins, GitHub Actions).
• Infrastructure automation with Terraform, AWS CDK, or CloudFormation.
• Strong understanding of data pipelines, ETL/ELT, and feature engineering.
• Bachelor’s degree in Computer Science/Data Engineering (or equivalent experience).

Nice-to-haves / Additional context

• Experience with ML pipeline orchestration, governance, and observability.
• Experience with ML lifecycle automation and distributed agile collaboration.