Hands-on experience with cloud platforms (AWS, Azure, or GCP).
Proficiency in MLOps tools like MLflow, Kubeflow, or SageMaker Pipelines.
Strong knowledge of Docker, Kubernetes, and container orchestration.
Proficient in Python, Bash, and basic scripting for automation.
Experience with CI / CD tools and version control systems (e.g., Git).
Familiarity with data and model versioning tools like DVC.
Good understanding of machine learning lifecycle and ML model management.
Knowledge of networking, security, and infrastructure automation.
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