Senior Solutions Architect, Nvidia Cloud Partners - Mexico

NVIDIA NVIDIA · Semiconductors · Mexico · Remote

This role involves acting as a technical advisor and driver for customers and partners in designing, implementing, and deploying large-scale AI/HPC GPU infrastructure and applications. It focuses on integrating libraries, frameworks, and models, and delivering GenAI, AI, and ML solutions to production, with practical expertise in fine-tuning and deploying models.

What you'd actually do

  1. Collaborate with NVIDIA Cloud Partners to create, implement, and deliver on NVIDIA's innovative hardware and software solutions.
  2. Partner with SAs, Account Managers, Engineering, Product, and business leaders to align on strategies, assess technical needs, secure business opportunities for NVIDIA.
  3. 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.
  4. Conduct regular technical customer meetings for project/product details, feature discussions, intro to new technologies, and debugging sessions.
  5. Work closely with customers to build and adopt NVIDIA solutions including PoCs to address critical business needs covering infrastructure, libraries, and applications.

Skills

Required

  • BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, Mathematics, or other Engineering fields or equivalent experience.
  • 8+ 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.

Nice to have

  • Practical experience with NVIDIA GPUs, software libraries, frameworks, and foundation models, such as [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/), [NVIDIA NeMo Framework](https://github.com/NVIDIA/NeMo), [NVIDIA Dynamo](https://www.nvidia.com/en-us/ai/dynamo/), [NeMo Retriever](https://developer.nvidia.com/nemo-retriever), [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server), [TensorRT](https://developer.nvidia.com/tensorrt), [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [NVIDIA CUDA-X](https://www.nvidia.com/en-us/technologies/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.

What the JD emphasized

  • 8+ years of Solution Engineering (or similar Sales Engineering, Cloud Engineering, Solution Architecture) including experience working directly with partners and customers.
  • Practical expertise fine tuning and deploying models, integrating software application stacks, libraries, and frameworks to drive consumption from GPU platforms.

Other signals

  • GPU infrastructure
  • AI/HPC
  • GenAI, AI, and ML hardware/software to production
  • Deep Learning, LLMs
  • fine tuning and deploying models