Deep Learning Solution Architect - Agentic Performance

NVIDIA is hiring a Deep Learning Solution Architect - Agentic Performance in Beijing, China. Level rates it ; you can apply on NVIDIA.

Level's read on this job

Builds AI ●●●● is how Level rates this solutions architect role: the job is building AI systems.
The posting mentions Hugging Face, PyTorch and Docker among its tools and skills.
Level classes it as a senior role.
NVIDIA has 2,800+ other open roles on Level.

AI in this role

Architect end-to-end enterprise generative AI solutions focusing on LLM pretraining, fine-tuning, RAG workflows, and agentic inference.

Hugging FacePyTorchHugging FaceDockerKubernetesTrt LLMMegatron LmNemo

ragai-agentsfine-tuningsolution-architecturellm-trainingagentic-inference

NVIDIA are seeking dynamic Solution Architects with specialized expertise in training Large Language Models (LLMs), implementing RAG workflows, and agentic inference. You will leverage the full NVIDIA software & hardware ecosystem to design, optimize, and deliver production-grade generative AI solutions for enterprise customers. With competitive salaries and a generous benefits package, we are widely considered to be one of the world’s most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous person with a real passion for technology, we want to hear from you.

What You Will Be Doing:

Architect end-to-end solutions focused on LLM pretraining, fine-tuning, high-performance inference, RAG workflows, and agentic inference orchestration using NVIDIA’s hardware and software platforms.
Collaborate with customers to understand their LLM-related business challenges and design tailored solutions aligned with the NVIDIA ecosystem.
Lead LLM training, distributed optimization, and performance tuning to achieve optimal throughput, latency, and memory efficiency.
Design and integrate RAG workflows and agentic inference pipelines into customer systems; provide technical guidance on best practices.
Collaborate with NVIDIA engineering teams to provide feedback and support pre-sales technical activities (workshops, demos).

What We Need to See:

Master’s / Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience.
4+ years hands-on experience in AI, focusing on open-source LLM training, fine-tuning, and production inference optimization.
Deep understanding of mainstream LLM architectures and proficiency in LLM customization via PyTorch, Hugging Face Transformers.
Solid knowledge of GPU computing, cluster architecture, and distributed parallel training/inference for LLMs.
Competency in agentic inference design and using AI agents to solve business challenges.
Strong communication skills, able to articulate complex technical concepts to technical and non-technical stakeholders.

Ways to Stand Out from the Crowd:

Hands-on experience with NVIDIA’s generative AI ecosystem (TRT-LLM, Megatron-LM, NVIDIA NeMo).
Advanced skills in LLM optimization (quantization, KV Cache tuning, memory footprint reduction).
Experience with Docker, Kubernetes for containerized LLM and agent workflow deployment on-prem.
In-depth knowledge of multi-GPU parallelism and large-scale GPU cluster management.

#deeplearning

How we rate this

Deep Learning Solution Architect - Agentic Performance at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Deep Learning Solution Architect - Agentic Performance is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in China, Beijing.
The job was posted on August 27, 2026.
Seniority: senior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsFine-tuningSolution ArchitectureLLM TrainingAgentic InferenceHugging FacePyTorch

Also common in solutions architect jobs at this level

Python AWS CUDA Machine learning TensorFlow Reinforcement learning

Pay

Typical solutions architect job: $169k

This posting doesn't list pay. Use the typical solutions architect figure when pay comes up. See solutions architect jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Tell me about a project where solution architecture was part of your work. What did you do?
Tell me about a project where llm training was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, AI agents, Fine-tuning, Solution Architecture, and LLM Training. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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