Job Description

1. Design, build, and maintain end-to-end machine learning pipelines for training, deployment, and monitoring.
2. Collaborate with data scientists to transform prototypes into production-grade ML solutions .
3. Implement and manage CI/CD workflows for ML models and applications.
4. Containerize ML applications using Docker and orchestrate with Kubernetes .
5. Deploy models on cloud platforms (AWS, Azure, GCP) using services like SageMaker, Vertex AI, or Azure ML.
6. Use MLOps tools such as MLflow, Kubeflow, Airflow, or TFX for workflow automation and model lifecycle management.
7. Monitor model performance and implement retraining strategies to prevent model drift.
8. Manage and optimize data pipelines for efficient feature engineering and training.
9. Implement logging, monitoring, and alerting systems for ML services in production.
10. Ensure security, scalability, and compliance of ML infrastructure.
11. Collaborate cross-functionally with DevOps, software engineers, and data engineers to align infrastructure with product requirements.

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