DevOps Engineer | SDE - 1 | SDE - 2

Preferable Location(s):

Pune, India | Mumbai, India | Chennai, India | Delhi, India | Hyderabad, India

Work Type:

Full Time

Salary Range:

$65K - $70K

Apply Now

Role: Senior / Staff Engineer — Accelerated Compute (GPU Infrastructure)

Function: Engineering

Industry: Cloud Infrastructure, Artificial Intelligence, High Performance Computing

About Company

You join an early-stage, venture-backed technology company building a cloud purpose-built for AI and high-performance workloads. The client has secured $9.5 million in seed funding to tackle the toughest infrastructure challenges in GPU compute. Engineers work on deep-tech problems that few teams attempt, shaping the foundation for next-generation AI development. Your contributions directly influence product direction and customer success.

Position Overview

You design and automate the GPU layer that powers a new AI-focused cloud platform. You shape core services, from virtualization to cluster automation, enabling customers to deploy high-performance workloads with one API call. Your work drives utilization, reliability, and scale across the entire infrastructure.

Role & Responsibilities

Own GPU enablement on the platform, designing, building, and automating systems that make high-performance compute effortless to use.
Implement and manage NVIDIA vGPU across hypervisors so multiple virtual machines share GPUs efficiently.
Extend Kubernetes to be GPU-aware, building resource allocators and device plugins for MIG instances to maximize utilization.
Partition GPUs with NVIDIA MIG, carving hardware-isolated instances and exposing them as rentable units.
Build pre-configured VM images optimized for GPU workloads, including drivers, MPI, NCCL, and kernel tunings.
Create tooling that lets customers spin up multi-node, InfiniBand-connected GPU clusters with a single API call.

Must have Criteria

Strong background in cloud, SRE, or compute-focused infrastructure engineering.
Deep hands-on experience with NVIDIA GPU technologies such as vGPU, MIG, and CUDA.
Solid understanding of hypervisors, including KVM, VMware, or XenServer.
Experience with high-performance networking (InfiniBand, RDMA, IPoIB).
Production-grade Kubernetes expertise; bonus if you have written schedulers or device plugins.

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