# Deep Learning Solution Architect - Agentic Performance

**Company**: NVIDIA
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/China-Beijing/Deep-Learning-Solution-Architect---Agentic-Performance_JR2024018?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_dac46377-125

## Description

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.

**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.

## Skills

### Required
- Large Language Models
- LLM training
- fine-tuning
- production inference optimization
- PyTorch
- Hugging Face Transformers
- GPU computing
- cluster architecture
- distributed parallel training
- inference
- agentic inference design
- AI agents

### Nice to have
- NVIDIA’s generative AI ecosystem
- TRT-LLM
- Megatron-LM
- NVIDIA NeMo
- LLM optimization
- quantization
- KV Cache tuning
- memory footprint reduction
- Docker
- Kubernetes
- multi-GPU parallelism
- large-scale GPU cluster management

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Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/China-Beijing/Deep-Learning-Solution-Architect---Agentic-Performance_JR2024018?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)