Platform Engineer - AI Infrastructure

About the role

Sarvam runs a large, multi-vendor GPU fleet that serves two demanding workloads on the same physical infrastructure: training jobs that span hundreds of GPUs, and inference services that must hold a flat p99 under production load. This role builds the platform that sits on top of that fleet - the scheduling, scaling, multi-tenancy, serving, and self-service layers that let our ML teams use thousands of GPUs without a human in the loop for every job.

What you'll do

• This is the surface area of the platform. You won't own all of it at once - you'll take a capability and build it end to end - but you should be excited by most of it
• The scaling and elasticity layer. Autoscaling for both training (elastic and gang scaling, scale-to-fit) and serving (queue-depth and utilization-driven)
• Scheduling and orchestration. The scheduler layer itself - Kueue, Volcano, Slurm-on-Kubernetes, or custom controllers - with gang scheduling, priority and preemption
• Multi-tenancy, RBAC, and isolation. The tenant model and namespacing, RBAC and policy, quota and fair-share enforcement
• Networking components. Platform-level network plumbing - CNI configuration and custom components, ingress and service routing, RDMA / SR-IOV exposure into pods

What you need

• Kubernetes
• Python
• Terraform
• Go
• Machine learning
• Networking

Good to have

• Plumbing