- Strong experience in MLOps, Machine Learning, and AI/ML lifecycle management
- Hands-on experience with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Expertise in ML model deployment, monitoring, and automation
- Experience with MLflow, Kubeflow, Airflow, or similar MLOps platforms
- Solid knowledge of Docker, Kubernetes, and CI/CD pipelines
- Experience with Cloud Platforms (AWS, Azure, or GCP)
- Knowledge of model versioning, feature stores, and model registries
- Experience with Git, DevOps practices, and Infrastructure as Code