> Markdown version of [/jobs/ext/1181839-senior-solutions-architect-nvidia-cloud-partners-telco](https://www.wearedevelopers.com/jobs/ext/1181839-senior-solutions-architect-nvidia-cloud-partners-telco). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Solutions Architect, NVIDIA Cloud Partners - Telco - **Company:** NVIDIA Ltd. - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $152,000.0 - $287,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Cloud Computing, Cloud Engineering, Nvidia CUDA, Computer Programming, Computer Engineering, Software Debugging, Python (Programming Language), Performance Tuning, Tensorflow, SAS (Software), Software Engineering, Software Systems, Systems Integration, Graphics Processing Unit (GPU), Google Cloud, Load Balancing, Performance Testing, Pytorch, Large Language Models, Deep Learning, Kubernetes, Information Technology, Slurm, Machine Learning Operations, TensorRT, Hardware Infrastructure, Software Library - **Published:** July 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3362e94b4d66d514 ## About the Role NVIDIA is seeking an experienced Solutions Architect to be a trusted technical advisor, bridging design to deployment of large-scale AI / HPC GPU infrastructure. Drive offtake and consumption by integrating libraries, frameworks, models, and software applications. Deliver GenAI, AI, and ML hardware/software to production with the most consequential customers and partners, supporting partners building next-gen GPU platforms. Own end-to-end technology solution integration with strategic customers and offer product strategy recommendations based on feedback. Profiles should be comfortable in a dynamic environment with experience in Deep Learning, LLMs, and GPU technologies. This role is an excellent opportunity to work in an interdisciplinary team at NVIDIA!, * BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, Mathematics, or other Engineering fields or equivalent experience. * 6+ years of Solution Engineering (or similar Sales Engineering, Cloud Engineering, Solution Architecture) including experience working directly with partners and customers. * Experience crafting and deploying large-scale cluster environments, hands-on experience designing, developing, delivering distributed Cloud architectures. * Strong fundamentals in programming, optimizations and software design, especially in Python and Deep Learning frameworks such as PyTorch and TensorFlow. * Practical expertise fine tuning and deploying models, integrating software application stacks, libraries, and frameworks to drive consumption from GPU platforms. * Motivation and skills to own and drive complex multi-disciplinary technical engagements with customers throughout the full customer lifecycle and cross-functional teams. * Efficient time management and capable of balancing multiple tasks. Excellent presentation, communication and collaboration skills. * Self-starter with a passion for growth, continuous learning, and sharing insights. Ways to stand out from the crowd: * Practical experience with NVIDIA GPUs, software libraries, frameworks, and foundation models, such as NVIDIA Nemotron, NVIDIA NeMo Framework, NVIDIA Dynamo, NeMo Retriever, NVIDIA Triton Inference Server, TensorRT, TensorRT-LLM, NVIDIA CUDA-X * Hands-on expertise with scaled AI cloud environments (e.g., AWS, Azure, GCP) and on-premises / hybrid infrastructure, in particular inference and training workloads. * Familiarity with NVIDIA hardware (such as GPUs, networking, storage) and systems technology such as NCCL, DCGM, UFM, Mission Control, Base Command Manager. * Proficiency with large-scale AI model training / deployment encompassing GPU systems, performance testing, AI benchmarking, fine tuning, strong focus on MLOps and cluster orchestration (SLURM, K8s, orchestrator, load balancing, cloud architecture). * Experience working with enterprise developers and strong customer-facing skills. ## Description * Collaborate with NVIDIA Cloud Partners to create, implement, and deliver on NVIDIA's innovative hardware and software solutions. * Partner with SAs, Account Managers, Engineering, Product, and business leaders to align on strategies, assess technical needs, secure business opportunities for NVIDIA. * Become the primary technical driver for customers during the design, development, construction, integration, and production of GPU Cloud infrastructure and applications throughout the entire customer lifecycle. * Conduct regular technical customer meetings for project/product details, feature discussions, intro to new technologies, and debugging sessions. * Work closely with customers to build and adopt NVIDIA solutions including PoCs to address critical business needs covering infrastructure, libraries, and applications. * Prepare and deliver technical content to customers including presentations, workshops, reference architectures, tutorials, publications. * Up to 40% travel may be required. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Your Next AI Needs 10,000 GPUs. Now What?](https://www.wearedevelopers.com/videos/1590-your-next-ai-needs-10-000-gpus-now-what) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)