About The Role

We are seeking an experienced DevOps Engineer to join our infrastructure team, with a strong focus on managing and optimizing GPU-based compute environments for machine learning and deep learning workloads. In this role, you will be responsible for the end-to-end infrastructure lifecyclefrom provisioning with Terraform/Ansible to deploying ML models using modern frameworks like Hugging Face and Responsibilities :

• Manage infrastructure using Terraform and Ansible
• Deploy and monitor Kubernetes clusters with GPU support (including NVIDIA drivers and H100 SXM integration)
• Implement and manage inferencing frameworks such as Ollama, Hugging Face, etc.
• Support containerization (Docker), logging (EFK), and monitoring (Prometheus/Grafana)
• Handle GPU resource scheduling, isolation, and scaling for ML/DL workloads
• Collaborate closely with developers, data scientists, and ML engineers to streamline deployments and Skill Set :
• 58 years of hands-on experience in DevOps and infrastructure automation
• Proven experience in managing GPU-based compute environments
• Strong understanding of Docker, Kubernetes, and Linux internals
• Familiarity with GPU server hardware and instance types
• Proficient in scripting with Python and Bash
• Good understanding of ML model deployment, inferencing workflows, and resource to Have :
• Experience with AI/ML pipelines
• Knowledge of cloud-native technologies (AWS/GCP/Azure) supporting GPU workloads
• Exposure to model performance benchmarking and A/B testing
  
  

(ref:hirist.tech)