DevOps Architect

Senior Solutions Architect - Generative AI

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA is seeking a Senior Solutions Architect who can operate fluently across two worlds: the AI Factory infrastructure stack-designing accelerated compute environments built on DGX/HGX, networking, storage, and orchestration -and the Generative & Agentic AI application stack -guiding customers through open-source frameworks, NeMo, NIM, and Blueprints to deliver production AI workloads.

You will be a trusted technical advisor to enterprise customers, partnering with sales, product, and engineering to translate business outcomes into deployable architectures.

What You'll Be Doing:

• Lead enterprise customers through the full lifecycle of on-prem Generative AI — from use case discovery and model selection to fine-tuning, deployment, and continuous evaluation.

• Architect production-grade RAG systems using NeMo Retriever, embedding and reranker NIMs, and vector databases such as Milvus, pgvector, and Weaviate, tuned for accuracy, latency, and cost at scale.

• Guide customers in selecting and customizing open-source foundation models like Llama, Mistral, Qwen, and Gemma, and lead them through fine-tuning workflows using NeMo Customizer, PEFT/LoRA, SFT, DPO, and RLHF.

• Compose and build agentic applications using frameworks like LangGraph, LlamaIndex, CrewAI, AutoGen, and NVIDIA AI Blueprints, covering use cases such as customer service automation, enterprise search, document intelligence, video analytics, software engineering agents, and industry-specific copilots.

• Advise on inference optimization with TensorRT-LLM, vLLM, and SGLang, including quantization, speculative decoding, and multi-LoRA serving.

• Champion responsible AI practices: guardrails (NeMo Guardrails), red-teaming, evaluation harnesses, and observability for LLM and agent systems.

• Help customers operationalize GenAI with accurate MLOps — versioning, CI/CD for prompts and models, drift detection, and human-in-the-loop feedback.

• Provide architectural mentorship on on-prem AI Factory deployments built around DGX BasePOD/SuperPOD, HGX-based OEM systems, Spectrum-X and Quantum InfiniBand networking, and high-performance storage.

• Scale clusters appropriately for combined fine-tuning and inference tasks. Build orchestration layers through Kubernetes, including the NVIDIA GPU Operator, Network Operator, and Run:ai. Apply Slurm as needed.

• Lead technical workshops, PoCs, and architecture reviews with enterprise customers as well as represent NVIDIA at customer briefings, industry events, and technical forums.

• Translate field findings into product feedback for NVIDIA engineering as well as build reusable assets such as reference architectures, deployment guides, and demos that scale across the SA community.

What We Need To See:

• 5–8 years of experience as a Solutions Architect, Field Engineer, ML Platform Engineer, or similar customer-facing technical role.

• Strong hands-on background with accelerated computing infrastructure: GPUs, high-speed interconnects (InfiniBand, RoCE), parallel file systems (Lustre, GPFS, WEKA, VAST, or DDN or equivalent experience), and data center fundamentals.

• Production experience with Kubernetes and at least one workload scheduler like Run:ai, Slurm, KubeFlow, or Volcano in GPU-accelerated environments.

• Practical depth in Generative AI: LLM training/fine-tuning, RAG architectures, inference optimization (TensorRT-LLM, vLLM, SGLang), and quantization.

• Experience with the NVIDIA AI Enterprise software stack (NeMo, NIM, Triton, RAPIDS, CUDA-X) or a strong desire and capability to learn rapidly.

• Experience crafting solutions for enterprise constraints: air-gapped environments, data sovereignty, security/compliance (SOC 2, HIPAA, FedRAMP-adjacent), and multi-tenancy.

• Excellent communication skills - able to present to CTOs and CIOs, then drop into a kubectl session with a platform engineer.

• Bachelor's or Master's in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).

Ways to Stand Out from the crowd:

• Hands-on experience with agentic frameworks (LangGraph, AutoGen, CrewAI, LlamaIndex agents) in production settings.

• Contributions to open-source AI/ML projects or published technical content (blogs, talks, papers).

• Experience working with NVIDIA AI Blueprints or developing equivalent enterprise reference solutions.

• Background in a specific vertical - financial services, healthcare/life sciences, manufacturing, retail, or telco.

• Familiarity with sovereign AI initiatives or large-scale on-prem GenAI deployments. Working knowledge of MLOps tooling (MLflow, Weights & Biases, Argo, Kubeflow Pipelines).

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Experience Level

Senior Level