- 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 environments.

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)
🏢 ValueMomentum
📍 Secunderabad