ML Ops Engineer
Core
Operate and monitor GPU workload orchestration on the Sferical.ai platform, ensuring 216+ GPUs are used efficiently, securely, and reliably for AI teams.
Role type
MLOps Engineer (Infrastructure)
Builds
AI infrastructure operations and GPU workload execution
Domain
Telecommunications / AI Infrastructure
Deliverable
infrastructure
Required skills
Kubernetes, GPU scheduling, object storage (S3), NFS, enterprise security, RBAC, API-based troubleshooting, capacity planning
Preferred skills
Run:ai, VAST Data, NVIDIA platforms, Slurm, distributed training frameworks
Responsibilities
Operate and monitor GPU workload orchestration using Run:ai; Troubleshoot Kubernetes clusters including pod scheduling and storage connectivity; Support data movement across storage systems; Monitor infrastructure health and produce capacity reports; Coordinate with users and vendors to resolve incidents; Prepare infrastructure for future scaling; Create operational guides and documentation
