MLOps Engineer

About the job

• *Role & responsibilities** • Proven 5-12 years of professional experience across MLOps, DevOps, or similar disciplines. • Knowledge in the life cycle management of Machine Learning models. • Proficiency with Machine Learning frameworks like TensorFlow, PyTorch, or Scikit-learn. • An in-depth understanding of contemporary software engineering techniques. • Docker, Kubernetes, and/or Python or R expertise. • Expertise in cloud computing systems, including AWS, Azure, and GCP. • Working experience with version control systems such as Git, automation tools, and CI/CD pipelines. • Knowledge of MLOps platforms, such as MLflow, Kubeflow, and SageMaker. • Familiarity with monitoring solutions and observability practices for ML systems.

Requirements

• MLOps
• DevOps
• TensorFlow
• PyTorch
• Docker
• AWS
• Azure

Preferred Technologies

• MLOps
• DevOps
• TensorFlow
• PyTorch
• Docker
• AWS
• Azure