Job Summary:

We are seeking an experienced AWS Engineer to manage and optimize the CI/CD pipeline for machine learning solutions. You will work closely with machine learning engineers, data scientists, and DevOps teams to ensure the efficient deployment, scaling, and monitoring of ML models on AWS. This role requires expertise in AWS services, DevOps practices, and machine learning infrastructure.

Key Responsibilities:

• CI/CD Pipeline Management & Automation:

1. Design and maintain CI/CD pipelines for ML models.
2. Automate deployment processes using AWS services (e.g.CodePipeline, CodeBuild, CodeDeploy).
3. Integrate model training, testing, and deployment stages within the pipeline.
4. Use infrastructure as code (IaC) tools like AWS CloudFormation or Terraform for scalable environments.
5. Automate model deployment, scaling, and monitoring using AWS Lambda, ECS, EKS, and SageMaker.

• AWS Cloud Services & Security:

1. Manage AWS services like EC2, S3, SageMaker, and Lambda to support ML pipelines.
2. Use SageMaker for data preparation, training, tuning, and model deployment.
3. Automate model retraining and versioning.
4. Ensure compliance with data privacy, security, and governance standards.

• Monitoring & Optimization:

1. Monitor ML model performance using AWS CloudWatch and CloudTrail.
2. Implement testing and monitoring processes for production models.
3. Optimize cost and performance with automated scaling and resource management.

• Collaboration & Support:

1. Collaborate with data scientists, ML engineers, and DevOps teams to integrate models.
2. Provide troubleshooting support for pipeline issues.
3. Work with security teams to ensure data and model protection.

Key Skills & Qualifications:

1. Bachelor’s degree in Computer Science or related field.
2. 5+ years of experience as an AWS/DevOps/Cloud Engineer.
3. Expertise in AWS services (S3, EC2, SageMaker, Lambda, etc.).
4. Experience with CI/CD tools like AWS CodePipeline and GitLab CI.
5. Proficiency in containerization (Docker, Kubernetes).
6. Knowledge of IaC tools like AWS CloudFormation or Terraform.
7. Familiarity with machine learning frameworks (TensorFlow, PyTorch).
8. Experience managing CI/CD pipelines for ML or data science solutions.
9. Strong understanding of version control systems (Git) and monitoring tools (AWS CloudWatch).
10. Excellent collaboration and communication skills