5+ Years of experience in an MLOps, DevOps, or similar engineering role focused on deploying and managing machine learning models.
Strong proficiency in Python programming for data manipulation, automation, and infrastructure-as-code.
Significant hands-on experience with AWS, including services such as EC2, S3, IAM, CloudFormation/Terraform, and containerization technologies (Docker, Kubernetes/EKS).
Experience with building and managing CI/CD pipelines (e.g., Jenkins, GitLab CI/CD, AWS CodePipeline).
Understanding of machine learning concepts, model evaluation metrics, and deployment strategies.
Experience with monitoring and logging tools (e.g., CloudWatch, Prometheus, Grafana)
Experience with version control systems (e.g., Git).
Hands-on experience with AWS SageMaker for model building, training, and deployment.
Experience with AWS Glue for ETL and data preparation.
Experience with other MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow).
Experience with infrastructure-as-code tools beyond CloudFormation (e.g., Terraform).

Please refer the Job description for details