2.0 - 5.0 years

0 - 0 Lacs

Nagpur

Remote

Key Responsibilities: Provision and manage GPU-based EC2 instances for training and inference workloads. Configure and maintain EBS volumes and Amazon S3 buckets (versioning, lifecycle policies, multipart uploads) to handle large video and image datasets . Build, containerize, and deploy ML workloads using Docker and push images to ECR . Manage container deployment using Lambda , ECS , or AWS Batch for video inference jobs. Monitor and optimize cloud infrastructure using CloudWatch, Auto Scaling Groups , and Spot Instances to ensure cost efficiency. Set up and enforce IAM roles and permissions for secure access control across services. Collaborate with the AI/ML, annotation, and backend teams to streamline cloud-to-model pipelines. Automate cloud workflows and deployment pipelines using GitHub Actions , Jenkins , or similar CI/CD tools. Maintain logs, alerts, and system metrics for performance tuning and auditing. Required Skills: Cloud & Infrastructure: AWS Services : EC2 (GPU), S3, EBS, ECR, Lambda, Batch, CloudWatch, IAM Data Management : Large file transfer, S3 Multipart Uploads, storage lifecycle configuration, archive policies (Glacier/IA) Security & Access : IAM Policies, Roles, Access Keys, VPC (preferred) DevOps & Automation: Tools : Docker, GitHub Actions, Jenkins, Terraform (bonus) Scripting : Python, Shell scripting for automation & monitoring CI/CD : Experience in building and managing pipelines for model and API deployments ML/AI Environment Understanding: Familiarity with GPU-based ML workloads Knowledge of model training, inference architecture (batch and real-time) Experience with containerized ML model execution is a plus Preferred Qualifications: 2-5 years of experience in DevOps or Cloud Infrastructure roles AWS Associate/Professional Certification (DevOps/Architect) is a plus Experience in managing data-heavy pipelines , such as drones, surveillance, or video AI systems

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