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Platform Engineer - AI Infrastructure

Sarvam· Infrastructure
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Bengaluru, ChennaiFull-timePosted 3mo ago

About the role

Platform Engineer responsible for building the control-plane, scheduling, scaling, and self-service layers on top of Sarvam's multi-vendor GPU fleet to support training and inference workloads with strong emphasis on Kubernetes internals, multi-tenancy, and GPU constraints.

Responsibilities

  • The serving platform. The control plane that turns a model artifact into a scalable, multi-tenant endpoint: intelligent routing and load balancing across replicas, rollout machinery (canary, blue-green, rollback), traffic splitting, model-version integration etc.
  • The scaling and elasticity layer. Autoscaling for both training (elastic and gang scaling, scale-to-fit) and serving (queue-depth and utilization-driven), capacity pooling across clusters, burst handling, preemption and reclaim, and efficient bin-packing across GPUs etc.
  • Scheduling and orchestration. The scheduler layer itself - Kueue, Volcano, Slurm-on-Kubernetes, or custom controllers - with gang scheduling, priority and preemption, queue fairness, quota enforcement, and topology-aware placement that keeps a job's ranks on the same fabric island.
  • Multi-tenancy, RBAC, and isolation. The tenant model and namespacing, RBAC and policy, quota and fair-share enforcement, MIG / MPS / time-slicing exposed as self-service tiers, secrets and credential management, and audit logging.
  • Networking components. Platform-level network plumbing - CNI configuration and custom components, ingress and service routing, RDMA / SR-IOV exposure into pods, tenant network policy and segmentation, and multi-cluster connectivity.
  • Observability and cost tooling. The metrics, logging, and tracing pipeline as a platform feature with cardinality managed at fleet scale.
  • The storage and data path. Abstractions over the parallel filesystem, caching tiers, volume provisioning, and data-locality-aware placement.
  • Developer experience and self-service. The layer ML engineers actually touch - CLI, SDK, and APIs, job-submission abstractions and inner-loop tooling.
  • Provisioning and infrastructure-as-code. The control plane that stands clusters up reproducibly - Terraform, Crossplane, or operators; node lifecycle and image management; driver and firmware rollout automation; and multi-vendor cluster bring-up.

Qualifications

  • 5+ years building infrastructure or platform software, with a track record of systems you designed and shipped - not scripts and glue, but services and control planes that others built on.

Skills

Required:Go or PythonKubernetes internalsGPU platform constraintsMulti-tenant design
Last updated Jul 14, 2026. Interested? Apply on the company site.
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Sarvam | India's Full-Stack Sovereign AI Platform

Sarvam is India's full-stack sovereign AI platform, with speech-to-text, text-to-speech, translation, and conversational agents across 22 Indian languages. Start free.

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