Head of GPU Cluster Engineering

Nava; Bengaluru; On-site; INR 6,000,000 - 11,000,000; Full time

Job summary

Nava is building next-generation AI infrastructure and inference platforms. We are looking for a Head of GPU Cluster Engineering to lead architecture, deployment, and operations of large-scale GPU clusters.

This leadership role defines the engineering vision, drives execution across infrastructure domains, and ensures GPU platforms deliver world-class performance and reliability.

Qualifications

• 12+ years in infrastructure engineering, distributed systems, cloud platforms, or AI infra.
• Proven experience leading large-scale infrastructure or platform engineering teams.
• Strong architectural thinking balancing technical excellence and business priorities.
• Excellent stakeholder management and cross-functional leadership.

Responsibilities

• Own end-to-end engineering outcomes from reference architecture to production operations.
• Define vision, architecture, and operating model for large-scale GPU infrastructure.
• Establish standards, design principles, and operational excellence across the platform.
• Drive scalability, resiliency, and performance for growing AI workloads.
• Lead cross-functional teams across GPU Compute, Networking, Storage, and SRE.

Skills

Leadership

Infrastructure engineering

GPU Compute

High-Speed Networking

Kubernetes

Distributed storage

Automation

Observability

Linux systems

Architectural thinking

Stakeholder management

Tools

CUDA

NCCL

Slurm

Ray

Kubeflow

Job description

About Nava

Nava is building next-generation AI infrastructure and inference platforms designed to power the future of AI. We are looking for a

About Nava

Nava is building next-generation AI infrastructure and inference platforms designed to power the future of AI. We are looking for a Head of GPU Cluster Engineering to lead the architecture, deployment, and operations of large-scale GPU clusters.

This is a highly strategic leadership role responsible for defining the engineering vision, driving execution across multiple infrastructure domains, and ensuring our GPU platforms deliver world-class performance, scalability, and reliability.

What You'll Do

Platform Leadership

• Own end-to-end engineering outcomes, from reference architecture through production operations.


• Define the technical vision, architecture, and operating model for large-scale GPU infrastructure.


• Establish engineering standards, design principles, and operational excellence across the platform.


• Drive platform scalability, resiliency, and performance to support rapidly growing AI workloads.

Architecture & Technical Strategy

• Lead the design and evolution of GPU cluster architectures across compute, networking, storage, orchestration, and observability.


• Evaluate and drive technology decisions around GPUs, interconnects, storage, Kubernetes, scheduling, and AI infrastructure software.


• Review and approve architecture decisions while balancing performance, reliability, cost, and operational complexity.


• Drive infrastructure standardization and automation across deployments.

Cross-Functional Leadership

• Lead execution across multiple engineering pillars:




• GPU Compute


• High-Speed Networking


• Storage


• Platform Engineering


• Site Reliability Engineering (SRE)


• Infrastructure Automation




• Partner closely with Product, Supply Chain, Data Centre Operations, and Customer Success teams to ensure successful platform delivery.


• Act as the technical escalation point for critical engineering decisions.

Delivery & Operational Excellence

• Own engineering readiness gates from design through production deployment.


• Drive release planning, operational reviews, risk assessments, and post-incident analysis.


• Establish SLAs, SLOs, and operational metrics for platform health and reliability.


• Champion automation, observability, incident management, and continuous improvement across the engineering organization.

Team Leadership

• Build, mentor, and scale a high-performing engineering organization.


• Develop technical leaders across infrastructure disciplines.


• Foster a culture of engineering excellence, ownership, collaboration, and innovation.


• Support hiring and talent development for critical infrastructure roles.

What We're Looking For

• 12+ years of experience in infrastructure engineering, distributed systems, cloud platforms, or AI infrastructure.


• Proven experience leading large-scale infrastructure or platform engineering teams.


• Deep understanding of GPU clusters, AI infrastructure, HPC, or large-scale distributed systems.


• Strong expertise across:




• GPU Compute (NVIDIA ecosystem preferred)


• High-performance networking (InfiniBand, RoCE, RDMA)


• Kubernetes and container orchestration


• Distributed storage systems


• Infrastructure automation and observability


• Linux systems and platform engineering




• Experience designing highly available, scalable production infrastructure.


• Strong architectural thinking with the ability to balance technical excellence and business priorities.


• Excellent stakeholder management and cross-functional leadership skills.

Nice to Have

• Experience building AI factories, GPU cloud platforms, or large-scale inference infrastructure.


• Familiarity with CUDA, NCCL, Slurm, Ray, Kubeflow, or similar AI infrastructure technologies.


• Experience working with hyperscalers, cloud providers, or AI-first technology companies.


• Exposure to multi-region or global infrastructure deployments.

Why Join Nava?

• Build one of the world's leading AI infrastructure platforms.


• Lead the engineering strategy behind next-generation GPU clusters and AI factories.


• Work alongside world-class engineers solving some of the most challenging infrastructure problems in AI.


• Shape the future of AI infrastructure from architecture through production at global scale.

Skills: leadership,automation,platforms,gpu,reliability,storage,architecture,cloud,infrastructure,cluster,drive