Responsibilities :
Design, build, and maintain scalable MLOps and DevOps solutions on AWS. Develop and manage CI/CD pipelines for ML model deployment and application releases. Automate infrastructure provisioning using Infrastructure as Code (IaC) tools. Deploy, monitor, and optimize machine learning models in production environments. Manage containerized applications using Docker and Kubernetes. Implement model versioning, experiment tracking, and monitoring frameworks. Ensure security, reliability, and high availability of cloud infrastructure. Collaborate with Data Scientists, ML Engineers, and Development teams to operationalize ML workflows. Troubleshoot deployment, performance, and infrastructure-related issues.
Technical and Professional Requirements:
Strong experience with AWS Cloud Services (EC2, S3, Lambda, IAM, EKS, ECS, CloudWatch, SageMaker). Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI. Experience in DevOps tools: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps. Proficiency in Python scripting and automation. Expertise in Docker and Kubernetes. Knowledge of Infrastructure as Code tools such as Terraform or CloudFormation. Experience with monitoring tools like CloudWatch, Prometheus, and Grafana. Strong understanding of Linux systems and networking concepts. Experience with Git version control.