- Design and implement CI/CD pipelines for ML models using AWS services on SageMaker AI
- Automate model training, validation, deployment, and monitoring workflows.
- Collaborate with data scientists to ensure models are production-ready and meet performance standards.
- Manage model versioning, reproducibility, and rollback strategies.
- Monitor model performance and system health using tools like CloudWatch, Prometheus, Grafana.
- Ensure security, scalability, and reliability of ML systems on AWS.
- Maintain documentation for ML workflows, infrastructure, and deployment processes.
- Continuously improve ML infrastructure and operational practices.
Required Qualifications:
- Proven experience in deploying and managing ML models in production environments.
- Strong understanding of CI/CD pipelines, DevOps, and MLOps principles.
- Hands-on experience with AWS services such as SageMaker, S3, Lambda, EC2, EKS, and CloudFormation.
- Proficiency in Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch).
- Experience with Docker, Kubernetes, and infrastructure-as-code tools like Terraform.
- Familiarity with monitoring tools and version control systems (e.g., Git).
- Experience with data versioning tools like DVC or MLflow.
- Knowledge of feature stores, model registries, and experiment tracking.
- Understanding of Python, matillion

Keyskills: Machine Learning AWS Python
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