DevOps Engineer, GPUaaS
Tasks
- Collaborate to streamline workflows and improve collaboration
- Conduct GPU cluster benchmark and track GPU technology advancements
- Design deploy and support GPU clusters for AI and ML workloads
- Design implement and manage CI CD pipelines for AI models and GPU accelerated applications
- Identify bottlenecks and improve development and operational processes for AI and HPC GPU cloud
- Implement security best practices for multi tenant GPUaaS
- Improve infrastructure provisioning management and monitoring through automation
- Manage and automate provisioning of GPU resources on prem and cloud
- Monitor cluster usage health performance and availability
- Optimize system parameters for AI workload performance
- Participate in rotational or scheduled shift work
- Provide technical support and guidance to users
- Set up monitoring and logging for GPU resources
- Solve problems in high performance distributed computation
- Troubleshoot compute resource system level issues
Perks/Benefits
- Flexible work arrangements
- Health and wellness benefits
- Internal mobility opportunities
- Training and development programs
Skills/Tech-stack
Ansible | Automation | Bash | CI/CD | CUDA | CentOS | Containers | Docker | GPU Acceleration | GPU Architecture | GPU drivers | IaaS | Infiniband | Jenkins | Kubernetes | Linux | Logging | MPI | Monitoring | NCCL | NVIDIA DCGM | NVIDIA GPUs | Networking | PaaS | Prometheus | PyTorch | Python | RDMA | Rocky Linux | Security | Slurm | TensorFlow | Terraform | Ubuntu | Zabbix
Roles
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