Strong understanding of machine learning concepts and hands on experience with ML frameworks TensorFlow, PyTorch, Scikit learn.
In depth experience with AWS SageMaker, including training, deploying, and tuning models.
Experience with AWS Model Registry or similar systems for managing ML model lifecycle.
Knowledge of AWS cloud architecture, including services like EC2, S3, Lambda, API Gateway, and RDS.
Expertise in CI/CD practices for model deployment using AWS CodePipeline, CodeBuild, or similar tools.
Familiarity with AWS security best practices, IAM roles, and encryption techniques for model and data protection.
Experience with AWS monitoring services CloudWatch, SageMaker Model Monitor and reporting tools AWS QuickSight, Athena.
Strong troubleshooting skills, ability to debug and resolve ML pipeline issues.
Proven ability to work in a DevOps or MLOps environment, focusing on automation and continuous improvement.