Jobs at NVIDIA

Lead technical Developer Relations for NVIDIA across AI Labs in India. Build platform adoption through developer relationships, technical workshops, demos, benchmarks, architecture reviews, and performance optimization. Assess AI workloads for GPU acceleration, debug integrations, recommend NVIDIA software stacks, and translate developer feedback into product requirements. Engage executives, researchers, infrastructure teams, and startup CTOs while coordinating with sales, product, engineering, marketing, and partners.

Architect end-to-end generative AI solutions centered on LLM training, fine-tuning, deployment, inference optimization, and RAG workflows. Collaborate with customers, partners, marketing, business development, and engineering teams to define tailored solutions. Lead technical workshops, training sessions, and design engagements while providing guidance on GPU-accelerated, cloud, on-premises, and scalable distributed deployments.

Leads technical Developer Relations for NVIDIA AI platforms across India's financial services AI labs. Builds adoption strategies, technical demos, benchmarks, reference architectures, workshops, and performance guides. Works directly with researchers, infrastructure teams, and CTOs to profile workloads, optimize inference, debug integrations, and design secure, observable GPU deployments across cloud, data centers, Kubernetes, and hybrid environments. Translates developer feedback and workload requirements into product and engineering input.

The role involves designing and maintaining AI/HPC infrastructure, focusing on large-scale networking projects, performance monitoring, troubleshooting, and customer interaction.

Lead and scale a network of technical partners and system integrators to deploy NVIDIA AI Factory solutions. Recruit and onboard SIs, build joint business plans, drive co-selling with enterprise account teams, enable partner technical proficiency and certification, manage partner-sourced pipeline, forecasting, and ecosystem health reporting to leadership.

Architect and build large-scale GPU cloud platforms (IaaS/PaaS/SaaS) for generative AI, design GPU cluster orchestration with Kubernetes and Slurm, implement resource scheduling, observability, and networking, advise customers on deploying and scaling LLM/ML workloads, and collaborate with engineering and executive teams to drive adoption and optimize performance.

Lead strategic partner engagement with major Indian conglomerates to identify, design, and scale enterprise AI workloads. Build executive and technical relationships, coordinate cross-functional NVIDIA teams, run workshops and architecture reviews, develop repeatable use cases, and capture account requirements and product feedback to deliver deployable, scalable AI outcomes across cloud, data center, edge, and industrial environments.

As a Senior Solution Architect for NVIDIA, you'll develop CI/CD pipelines, automate large-scale infrastructure, and implement monitoring solutions while troubleshooting and improving standard methodologies.

The Senior Solution Architect will develop and automate deployment pipelines, manage large-scale AI/HPC infrastructure, troubleshoot systems, and engage with customers to implement networking projects.

Design, build, and operate a Kubernetes-based platform for global network infrastructure. Own cluster lifecycle, provisioning, upgrades, GitOps delivery, observability, capacity, and recovery. Develop automation, provide production support and on-call incident response for network services, and drive issues from detection through verified resolution.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

Design, implement, and operate scalable distributed software and network/system automation for NVIDIA's multi-tenant cloud infrastructure. Own code lifecycle, build maintainable APIs, ensure secure communications and data persistence, contribute to open-source, and present roadmaps to stakeholders.