Role at a glance
- Salary
- $152K – $287.5K/yr
- Location
- Santa Clara, California, United States
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- 5+ Years in Solutions Architecture
- Education
- BS in CS/Engineering or equivalent experience.
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Role Summary
The Solutions Architect will help roll out and enhance AI inference solutions at scale using NVIDIA GPU technology and Kubernetes. The role partners with engineering, DevOps, and customers to develop enterprise generative AI solutions and deploy disaggregated inference systems to production.
What You'll Do
- Build inference pipelines with tools like NVIDIA Dynamo and distribute tasks among GPU workers to improve efficiency.
- Collaborate with DevOps teams to orchestrate disaggregated inference using Kubernetes for complex workloads.
- Accelerate inference pipelines using TensorRT-LLM, vLLM, SGLang, and other backends.
- Provide mentorship and technical leadership to customers and internal teams deploying disaggregated inference systems.
- Resolve complex issues related to disaggregated inference deployments.
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
5+ years in Solutions Architecture, including deploying distributed systems and AI inference workloads on Kubernetes. Experience with NVIDIA Dynamo, Triton Inference Server, or TensorRT-LLM; GPU orchestration with NVIDIA GPU Operator, NIM Operator, and MIG partitioning; GPU allocation, memory hierarchies, RDMA, UCX, and tuning large language models for low-latency inference.
Required
- 5+ Years in Solutions Architecture
- Deploying distributed systems and AI inference workloads on Kubernetes
- Experience with one of NVIDIA Dynamo, Triton Inference Server, or TensorRT-LLM
- GPU orchestration using NVIDIA GPU Operator, NIM Operator, and Multi-Instance GPU (MIG) partitioning
- Solving sophisticated GPU allocation, memory hierarchies (HBM, DRAM, SSD), and low-latency networking (RDMA, UCX)
- Tuning large language models for low-latency inference in enterprise environments
- BS in CS/Engineering or equivalent experience
Preferred
- Prior experience deploying NVIDIA inference technologies such as Dynamo, NIM, NIXL and Grove
- Deep understanding of transformer neural network and inference acceleration technologies like quantization, speculative decoding, WideEP...
- NVIDIA Certified AI Engineer or similar credentials
- Contributions to open-source projects including NVIDIA Dynamo, vLLM, KServe, or SGLang
Original job description
Content provided by the employer
Original job description
Content provided by the employer
We’re forming a team of innovators to roll out and enhance AI inference solutions at scale, demonstrating NVIDIA’s GPU technology and Kubernetes. As a Solutions Architect focused on inference, you’ll collaborate closely with our engineering, DevOps, and customers to develop enterprise AI solutions. Together, we'll deliver generative AI to production!
What you'll be doing:
Build inference pipelines with tools like NVIDIA Dynamo, distributing tasks among GPU workers to improve efficiency.
Collaborate with DevOps teams to orchestrate disaggregated inference using Kubernetes for complex workloads.
Accelerate inference pipelines using TensorRT-LLM, vLLM, SGLang, and other backends to ensure seamless integration with disaggregated inference.
Provide mentorship and technical leadership to customers and internal teams, guiding them through the deployment of disaggregated inference systems and resolving complex issues.
What we need to see:
5+ Years in Solutions Architecture with a proven track record of deploying distributed systems and AI inference workloads on Kubernetes.
Experience with one of NVIDIA Dynamo, Triton Inference Server, or TensorRT-LLM for model optimization and serving.
GPU orchestration using NVIDIA GPU Operator, NIM Operator, and Multi-Instance GPU (MIG) partitioning.
Solving sophisticated GPU allocation, memory hierarchies (HBM, DRAM, SSD), and low-latency networking (RDMA, UCX).
Demonstrated success in tuning large language models for low-latency inference in enterprise environments.
BS in CS/Engineering or equivalent experience.
Ways to stand out from the crowd:
Prior experience deploying NVIDIA inference technologies such as Dynamo, NIM, NIXL and Grove.
Deep understanding of transformer neural network, and inference acceleration technologies like quantization, speculative decoding, WideEP etc.
NVIDIA Certified AI Engineer or similar credentials.
Contributions to open-source projects including NVIDIA Dynamo, vLLM, KServe, or SGLang.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.About the company
NVIDIA
Large Enterprise
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.