Mlops Engineer Jobs

About The Position

Requirements

• Bachelor’s or Master’s in Computer Science, Machine Learning, or related field.
• 5+ years of professional experience in ML engineering, MLOps, or data-pipeline development.
• Proven ability to design and automate end-to-end ML pipelines in the cloud.
• Strong Python and SQL skills.
• Experience integrating ML systems with enterprise data sources (Oracle, Snowflake).
• Familiar with containerized deployments, workflow orchestration, and CI/CD.
• Understanding of model lifecycle management, versioning, and deployment best practices.

Responsibilities

• Build and automate ML pipelines for data preparation, training, inference, and retraining.
• Develop and maintain data pipelines between Oracle ERP, Snowflake, and cloud environments.
• Create Kedro-based modular pipelines for reusable and maintainable workflows.
• Use AWS Glue, DMS, Athena, and dbt for ETL and data transformation.
• Manage AWS Batch and Fargate workloads for scalable model training and inference.
• Integrate advanced data-science and forecasting libraries into production workflows.
• Implement CI/CD pipelines for ML and data workflows (GitHub Actions, Jenkins, etc.).
• Use MLflow for experiment tracking, model registry, and artifact management.
• Build and maintain Dockerized environments via AWS EC2, ECR, and Batch.
• Collaborate with data scientists to operationalize models and optimize performance.
• Ensure secure, compliant cloud deployments (IAM, RBAC, encryption, network security).