NVIDIA

1 month ago

Senior Solutions Architect, GPU Cloud GenAI – Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Architect and build scalable GPU cloud infrastructure (IaaS, PaaS, SaaS) for GenAI workloads, guiding enterprise customers through large-scale GPU deployments. Lead multi-tenant platforms, define orchestration with Kubernetes and Slurm, and collaborate with cross-functional teams to translate AI requirements into robust infrastructure foundations for on-premises and hybrid environments.

Location: Mumbai

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key Responsibilities

•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.

Key Requirements

•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.
•Proficiency in Kubernetes (GPU operator, device plugins, multi-tenancy) and Slurm for HPC and AI workloads.
•Hands-on experience with infrastructure-as-code (Terraform, Helm, Ansible), CI/CD pipelines, and observability stacks (Prometheus, Grafana, DCGC).
•Strong coding ability in Python and/or Go/C++, building platform tooling and automation from scratch.

Experience:5+ years

Education:Bachelor's

Skills:Communication

Tech Stack:KubernetesSlurmTerraformHelmAnsiblePrometheusGrafanaPythonGoC++InfiniBandRoCERDMA

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 month ago

Senior Solutions Architect, GPU Cloud GenAI – Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: Mumbai

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key Responsibilities

•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.

Key Requirements

•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.
•Proficiency in Kubernetes (GPU operator, device plugins, multi-tenancy) and Slurm for HPC and AI workloads.
•Hands-on experience with infrastructure-as-code (Terraform, Helm, Ansible), CI/CD pipelines, and observability stacks (Prometheus, Grafana, DCGC).
•Strong coding ability in Python and/or Go/C++, building platform tooling and automation from scratch.

Experience:5+ years

Education:Bachelor's

Skills:Communication

Tech Stack:KubernetesSlurmTerraformHelmAnsiblePrometheusGrafanaPythonGoC++InfiniBandRoCERDMA

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor