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

Location: Bengaluru, India (Hybrid/Remote)

Type: Full-time

Industry: AI infrastructure, Cloud Computing

About Company

This role is with a rapidly growing AI infrastructure startup founded in 2025 in Bengaluru by a leadership team with deep product, cloud, and systems experience from global-scale tech companies. The company has built a GenAI-powered private cloud platform that automates and manages complex AI workloads across hybrid, on-prem, edge, and sovereign cloud environments — designed for enterprise sectors where performance, data security, and compliance are critical. Backed by leading global VCs and prominent operators (approx. $10M seed raised), the company is recognized for strong engineering rigor and product clarity. Its platform focuses on AI-native orchestration, deep observability, and cost/performance optimization to help large enterprises deploy and scale AI with confidence. This is an opportunity to join early and shape the future of AI-first cloud infrastructure.

Position Overview

You own the GPU infrastructure that powers a new kind of AI-focused cloud. You design and automate systems that make high-performance compute as simple as an API call. You join early, influence architecture, and shape how enterprise customers run massive AI workloads.

Role & Responsibilities

Own end-to-end GPU enablement across the platform, from design to production support.
Implement and manage NVIDIA vGPU across multiple hypervisors so several VMs share GPUs efficiently.
Extend Kubernetes to become GPU-aware and build custom device plugins for MIG instances.
Use NVIDIA MIG to partition GPUs into hardware-isolated slices exposed as rentable units.
Create and maintain pre-configured VM images optimized for GPU workloads, including drivers and libraries such as MPI and NCCL.
Develop automation that lets customers spin up multi-node, InfiniBand-connected GPU clusters with a single API call.

Must have Criteria

Proven background in cloud, SRE, or infrastructure engineering with a compute focus.
Hands-on expertise with NVIDIA GPU technologies such as vGPU, MIG, and CUDA.
Strong knowledge of hypervisors including KVM, VMware, or XenServer.
Experience with high-performance networking technologies like InfiniBand, RDMA, and IPoIB.
Production-grade Kubernetes skills.

Nice to Have

Experience writing custom Kubernetes schedulers or device plugins.

FAQs

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