Senior Solutions Architect, GPU Cloud GenAI – Infrastructure

NVIDIA is seeking an experienced Solutions Architect & Engineer (SAE) with deep expertise in large-scale GPU cluster infrastructure and generative AI enablement. As a pivotal member of our Infrastructure and Platform Engineering team, you will architect and build the GPU cloud platforms (IaaS, PaaS, SaaS) that power the world's most demanding AI workloads. This position sits at the intersection of large-scale infrastructure engineering and applied AI, requiring both technical depth in platform development and the ability to guide enterprise customers through complex GPU infrastructure deployments.

The work location for this role is in Mumbai.

What you will be doing:

Design and architect scalable IaaS, PaaS, and SaaS layers for large-scale GPU cluster environments (32+ HGX/DGX nodes), spanning compute, networking, and storage orchestration.

Build multi-tenant GPU cloud platforms with production-grade APIs, control planes, and platform services that abstract infrastructure complexity for end users and application teams.

Develop cluster orchestration pipelines using Kubernetes (GPU operators, device plugins, multi-tenancy) and Slurm, optimizing for performance, reliability, and resource efficiency at scale.

Define and implement best practices for GPU resource scheduling, isolation, quota management, and observability, ensuring secure multi-tenant isolation and compliance.

Advise customers on deploying and scaling generative AI workloads (LLMs, MLLMs, RAG pipelines) on your infrastructure platforms, translating AI requirements into infrastructure specifications.

Engage with C-level executives and infrastructure teams to understand requirements, deploy GPU clusters across on-premises and hybrid cloud environments, and drive platform adoption.

Collaborate with NVIDIA engineering teams to resolve deep infrastructure bugs, provide feedback on platform capabilities, and influence product roadmap decisions.

Partner with customer infrastructure teams to tune, scale, and optimize GPU clusters for cost efficiency, throughput, and AI workload performance.

What we need to see:

5+ years of hands-on infrastructure or platform engineering experience, with demonstrated expertise designing and operating large-scale GPU clusters (100+ nodes).

Deep expertise building IaaS, PaaS, and SaaS platform layers—architecting and developing infrastructure foundations, not consuming cloud services.