NVIDIA
Senior Solutions Architect, Generative AI Deployment and AIOps
$184K–$288K
US, CA, Santa Clara +5 · Senior · Full-time · Posted Sep 16, 2026
- Location
- US, CA, Santa Clara
US, NY, Remote
US, IL, Remote
US, CA, Remote
US, MA, Remote
US, MD, Remote - Experience
- 8+ years of hands-on experience with deep learning frameworks.
- Education
- BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience).
About the job
Advises customers on deploying and optimizing generative AI and LLM inference solutions using NVIDIA platforms, including performance analysis on Kubernetes.
What you'll do
- Partner with customers and internal teams to define and build high-value NVIDIA AI solutions.
- Help customers adopt NVIDIA technology and MLOps solutions, engaging with developers, researchers, and data scientists.
- Analyze performance and power efficiency of AI inference workloads on Kubernetes; travel to conferences and customers may be required (20%).
What they ask for
- 8+ years of hands-on experience with deep learning frameworks such as PyTorch and TensorFlow.
- Strong programming, optimization, and software design fundamentals, especially in Python.
- Proficiency debugging GPU orchestration and Multi-Instance GPU (MIG) management within Kubernetes environments.
- Experience with containerization and orchestration technologies, monitoring, and observability solutions for AI deployments; excellent knowledge of LLM and deep learning inference.
Nice to have
- Prior experience with deep learning training at scale or deploying or optimizing inference in production.
- Experience with NVIDIA GPUs and libraries such as NVIDIA NIM, Dynamo, TensorRT, and TensorRT-LLM; excellent C/C++ skills or familiarity with parallel programming and distributed computing.
Pay
184,000 USD - 287,500 USD (base salary)
Tools
PyTorch, TensorFlow, Python, Kubernetes, NVIDIA GPUs, NVIDIA NIM, Dynamo, TensorRT
AI focus
Customer-facing deployment and optimization of generative AI and LLM inference; performance and power-efficiency analysis of AI inference workloads.
Model-written summary · the original posting is authoritative