Must have: • Expertise in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.). • Strong Experience in deployment/devops technologies: CI/CD pipelines, Kubernetes/Docker, and infrastructure-as-code tools (Terraform, Ansible, etc.). and cloud-native architectures (GCP and Aruze), monitoring and observability for ML workloads • Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX. Nice to have: • Experience with distributed computing frameworks (e.g., Spark, Ray, Dask) is a plus. • Familiarity with model explainability, fairness, and bias detection tools is highly desirable. • Strong knowledge of security best practices for ML systems, including data encryption, API security, and governance.