Solutions Architect, Inference Deployments

NVIDIA • Santa Clara, CA

1d

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• You will also be eligible for equity and benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level