Model Deployment: Deploy, monitor, and manage machine learning models in AWS environments (SageMaker, EC2, Lambda).
Automation: Develop and maintain CI/CD pipelines for ML workflows using tools like Gitlab,AWS CodePipeline, CodeBuild, and Jenkins.
Infrastructure Management: Design and manage scalable, reliable, and cost-effective AWS infrastructure for ML workloads (S3, RDS, DynamoDB, etc.).
Monitoring and Logging: Implement monitoring and logging solutions to ensure models are performing as expected (CloudWatch, Sagemaker Model Monitor).
Collaboration: Work closely with Data Scientists and DevOps teams to integrate ML models into production settings.

Requirements
4+ years of experience in MLOps, DevOps, or related fields.
Should be able to drive the requirements, follow-up,



collaboration with cross teams
Hands-on experience with AWS services like SageMaker, EC2, Lambda, S3, and RDS.
Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, or Scikit-Learn.
Experience with CI/CD tools and best practices
Familiarity with Infrastructure as Code (IaC) using tools like Terraform or AWS CloudFormation.
Knowledge of data engineering tools and practices.
Knowledge of Kubernetes or Docker.
Effective communication and collaboration skills, with the ability to effectively interact with stakeholders at all levels.

📌 Aws Mlops Engineer Secunderabad (India)
🏢 ValueMomentum
📍 India