Location : Kolkata
Robust understanding of the end-to-end machine learning lifecycle (data preparation, training, validation, deployment, and monitoring).
Experience building CI/CD and continuous training pipelines for ML workflows.
Hands-on expertise with Docker, Kubernetes, MLflow, and Kubeflow.
Monitoring and observability of ML systems, including model drift and performance tracking.
Experience with cloud platforms such as AWS, Azure, or GCP.
Knowledge of security, governance, and compliance for production ML systems.
Programming skills in Python, Java, or .NET, with frameworks such as TensorFlow and PyTorch.