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.