Description

You will design, deploy, and maintain scalable infrastructure for training and deploying machine learning models.

This role is hybrid.

Responsibilities

• Develop and maintain CI/CD pipelines for deep learning model training, testing, and deployment.
• Design scalable, distributed infrastructure to support high-performance training on GPUs/TPUs.
• Automate provisioning of cloud-based and on-premises deep learning clusters using infrastructure-as-code tools.
• Manage containerized environments and orchestration systems for AI workloads.
• Implement and maintain monitoring and logging systems for model performance and infrastructure health.

Required Skills

• 7+ years of experience as a DevOps Engineer, preferably in machine learning or data science environments.
• Strong experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
• Proficiency in cloud platforms AWS, GCP, or Azure, with a focus on GPU-based instances.
• Expertise in containerization (Docker) and orchestration (Kubernetes).
• Experience with infrastructure-as-code tools like Terraform, CloudFormation, or Ansible.
• Solid programming skills in Python, Bash, or Go.
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Strong understanding of GPU/TPU optimization for large-scale model training.

Preferred Skills

• Experience with MLOps tools like MLflow, Kubeflow, or Sagemaker.
• Familiarity with distributed training strategies (e.g., Horovod, DeepSpeed).

Key Skills

Education

Not specified