Jobs at NVIDIA

Architect and deliver generative AI solutions centered on LLM training, optimization, deployment, and RAG workflows. The role involves customer collaboration, technical workshops, pre-sales presentations, solution integration, and technical leadership. Responsibilities include fine-tuning models, optimizing GPU-based inference, designing scalable training workflows, and partnering with NVIDIA engineering teams. Candidates need deep expertise in language models, TensorFlow, PyTorch, Hugging Face, GPU clusters, and production deployments, with cloud, containerization, and Kubernetes experience preferred.

Supports and operates NVIDIA’s large-scale on-premises and cloud network infrastructure in a 24/7 global operations environment. Responsibilities include monitoring alerts, resolving incidents within SLAs, troubleshooting network issues, managing IP infrastructure and datacenter interconnects, coordinating vendors, implementing upgrades and capacity expansions, improving operational workflows, and documenting best practices. The role requires extensive networking protocol knowledge, cloud experience, automation exposure, and strong incident-management and communication skills.

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.

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.

Architects, implements, and supports advanced SAP IBP and APO supply chain planning solutions. Leads process improvements, prototypes, testing, upgrades, training, data analysis, and optimization modeling for supply and demand planning. Collaborates with business and IT teams to deliver scalable solutions, improve profitability, and support semiconductor industry planning needs. The role also mentors stakeholders and influences senior leadership on system adoption and operational improvements.

Design and ship Java, Go, and Rust services for NVIDIA Cloud Functions, a distributed platform routing AI workloads across GPU fleets. Improve performance, reliability, scalability, and cloud-native build and release processes. Collaborate across NVIDIA technologies, contribute to an open-source project, triage community issues, review pull requests, and write documentation. The role requires expertise in systems programming, distributed architecture, Kubernetes, containerization, Linux internals, scripting, and continuous integration.

Own and resolve complex Slurm support cases for production AI and HPC clusters. Diagnose scheduler, Linux, networking, storage, authentication, database, container, and GPU infrastructure issues; advise customers on configuration, upgrades, reliability, and incident recovery; collaborate with engineering on defects; and develop technical documentation, diagnostic tools, and training.

Design, develop, operate, and optimize large-scale software-defined networking solutions for NVIDIA AI Cloud environments. Responsibilities include building OVS/OVN control and data plane software, Kubernetes networking services, observability systems, CI/CD pipelines, GitOps integrations, and secure gRPC/REST services. The role owns production reliability, incident response, performance tuning, and upstream open-source contributions while collaborating with SRE, DevOps, and network engineering teams.

Design, develop, and deploy AI-powered automation for hardware performance verification. Build intelligent tools and agents for test generation, regression detection, failure analysis, and corrective recommendations. Integrate LLM assistants, create verification data pipelines, establish impact dashboards, and collaborate with verification engineers. Apply generative AI, reinforcement learning, and formal methods to improve coverage, reduce cycle times, and increase productivity across complex GPU, CPU, memory, and HPC verification workflows.

Lead the architecture and development of scalable full-stack Sales AI applications. Integrate generative AI models, retrieval systems, APIs, and agentic workflows into secure production solutions. Guide architecture across front-end, back-end, data, integrations, and cloud infrastructure while improving testing, delivery, observability, reliability, and maintainability. Lead technical decisions, investigate complex issues, mentor engineers, and collaborate with product, AI, data, security, and engineering teams.

Lead post-launch operations for Sales AI applications, ensuring availability, performance, security, governance, support readiness, and continuous improvement. Establish incident, problem, change, release, and lifecycle processes; monitor service and adoption metrics; develop automation, observability, self-service capabilities, and production-readiness practices. Partner with Sales, Product, Engineering, Data, Security, and IT teams to improve user experience, platform reliability, adoption, and business value.

Design, build, and evolve secure, scalable full-stack sales applications incorporating generative AI, intelligent workflows, APIs, data services, and cloud infrastructure. Lead architecture and technical design decisions, integrate AI models and retrieval systems, improve testing, CI/CD, observability, and production reliability, and mentor engineers. Collaborate with Product, AI/ML, Data, Security, Solution Architecture, and Engineering teams to deliver enterprise applications globally.

Architect and implement OpenBMC-based management for server platforms integrating BMCs, host CPUs, and top-of-rack Ethernet switches. Develop D-Bus services, Redfish APIs, object models, telemetry, security, firmware, and L2 networking capabilities. Build backend abstractions supporting switchdev, SAI, SONiC, DENT, and vendor SDKs. Collaborate across hardware, security, firmware, QA, manufacturing, and networking teams to deliver reusable, testable platform-management architecture.

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.