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Machine Learning Engineer, Enterprise MLOps - CIBC
ML Engineer
Machine Learning Engineer focused on enterprise MLOps, designing and maintaining scalable ML pipelines on AWS using Docker, Kubernetes, and CI/CD to automate data ingestion, model training, and deployment.
About the role
Key Responsibilities
- Design, build, and maintain end‑to‑end MLOps workflows for enterprise‑grade machine learning models.
- Automate data ingestion, feature engineering, and model training pipelines using Python, Docker, and Kubernetes.
- Implement CI/CD pipelines for model versioning, testing, and deployment on AWS services (SageMaker, ECS, EKS).
- Collaborate with data scientists and software engineers to integrate models into production systems.
- Monitor model performance, troubleshoot issues, and iterate on pipeline improvements.
Requirements
- Strong experience with Python and popular ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
- Hands‑on expertise in MLOps tools: Docker, Kubernetes, Airflow, MLflow, or similar.
- Proficiency with AWS services (SageMaker, ECS, EKS, S3, Lambda) and CI/CD tools (GitHub Actions, Jenkins, ArgoCD).
- Solid understanding of data pipelines, feature stores, and model monitoring.
- Excellent problem‑solving skills and ability to work collaboratively in a fast‑paced environment.
Skills
pythonmachine learningmlopsdockerkubernetesawscicd