build, deploy, and support scalable machine learning and GenAI platforms in production environments. Key Responsibilities Build and manage ML infrastructure using AWS, Azure, Kubernetes, Docker, and Terraform. Develop and maintain ML pipelines, CI/CD workflows, model deployment, versioning, and automated retraining. Manage model lifecycle using MLflow, SageMaker, Azure Machine … with engineering, data science, security, and DevOps teams to improve ML platform reliability and deployment efficiency. Required Skills Python, AWS, Azure, SageMaker, Azure ML, Kubernetes, Docker, Terraform, MLflow, Airflow, Kubeflow, GitHub Actions, Jenkins, FastAPI, Prometheus, Grafana, CI/CD, MLOps, LLMOps, RAG, and Model Monitoring. ...