AI Infrastructure System Engineer

Accountabilities:

• • Design and build fleet automation systems capable of provisioning, validating, deploying, upgrading, repairing, and retiring GPU clusters with minimal human intervention.
  • Develop AI infrastructure agents that automate deployment workflows, investigate root causes, triage incidents, and support autonomous remediation.
  • Build fleet intelligence platforms that continuously monitor hardware health, firmware, networking, storage, thermals, and workload performance.
  • Develop predictive capabilities that identify potential infrastructure failures before they affect customers or workloads.
  • Build software and automation systems that maximize GPU availability, utilization, performance, and reliability across large accelerator fleets.
  • Create automated validation frameworks for GPUs, InfiniBand/RoCE fabrics, NVLink/NVSwitch, storage systems, and distributed AI workloads.
  • Develop internal infrastructure platforms and developer tools that enable infrastructure to be managed programmatically rather than through manual operations.
  • Continuously improve deployment velocity, system reliability, and operational efficiency through automation and software engineering.
  • Collaborate with hardware, networking, platform, and AI teams to identify infrastructure challenges and develop scalable solutions.
  • Apply strong systems thinking to problems spanning hardware and software components across large-scale AI infrastructure.

  Requirements:

  • 3+ years of experience building distributed systems, infrastructure platforms, or large-scale backend software.
  • Strong software engineering skills in Python, Go, or Rust.
  • Proven experience developing platforms, automation systems, developer infrastructure, or similar software-driven infrastructure solutions.
  • Experience working with Linux and modern infrastructure technologies such as Kubernetes, Terraform, Ansible, or comparable tools.
  • Strong understanding of distributed systems and the ability to reason across hardware and software layers.
  • Passion for solving complex infrastructure problems through software and automation.
  • Strong automation-first mindset, with an instinct to build systems that eliminate repetitive manual tasks.
  • Ability to work effectively on complex technical challenges involving reliability, performance, scalability, and operational efficiency.
  • Strong collaboration skills and the ability to partner effectively with engineering teams across infrastructure, hardware, networking, and AI.
  • Experience with GPU infrastructure, CUDA, NCCL, NVLink/NVSwitch, or related technologies is a plus.
  • Experience with InfiniBand or RoCE networking is advantageous.
  • Familiarity with bare-metal provisioning and infrastructure lifecycle management is desirable.
  • Experience supporting large-scale AI training or inference clusters is a plus.
  • Knowledge of hardware health monitoring and predictive failure detection is beneficial.
  • Experience with distributed storage systems is advantageous.
  • Familiarity with AI agents or autonomous infrastructure operations is a plus.

  Benefits:

  • Opportunity to work on large-scale AI infrastructure supporting advanced training and inference workloads.
  • Exposure to complex systems spanning GPUs, networking, storage, distributed computing, and AI workloads.
  • Opportunity to build highly automated infrastructure systems and developer platforms.
  • Collaboration with multidisciplinary engineering teams working across hardware, networking, platform, and AI.
  • Environment focused on software-driven infrastructure, automation, scalability, reliability, and performance.
  • Opportunity to contribute to systems operating at significant GPU scale.
  • Remote-friendly job listing based in Bangalore, India.