- Robust experience in Python and Machine Learning frameworks.
- Hands-on experience with MLOps tools such as MLflow, Kubeflow, Airflow, Jenkins, GitHub Actions, or Azure ML.
- Experience with Docker, Kubernetes, and container orchestration.
- Strong understanding of CI/CD pipelines and DevOps practices.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Knowledge of model monitoring, model governance, and automated retraining.
- Experience with version control systems such as Git.
- Understanding of Data Engineering and ML lifecycle processes.