Solution Architect - Agentic AI - CSP

NVIDIA
Beijing, Shenzhen
Workplace: OnsiteFull timeFunction: Solutions Engineering & Sales EngineeringExperience: 3+ yearsEducation: mastersSkills: ["Technical communication","Structured thinking","Problem-solving"]

Design, deploy, and optimize large-scale LLM and agentic AI inference solutions on NVIDIA accelerated platforms. Lead end-to-end inference workload analysis to improve latency, throughput, scalability, resource utilization, and cost efficiency, while building proof-of-concepts and reference architectures. Collaborate with cloud providers and NVIDIA engineering, product, sales, and business development to translate customer requirements into scalable solutions and product feedback.

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FursaFursa
NVIDIA
NVIDIA
3 days ago

Solution Architect - Agentic AI - CSP

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Last checked: 4 hours agoStatus: Live

Job Summary

Design, deploy, and optimize large-scale LLM and agentic AI inference solutions on NVIDIA accelerated platforms. Lead end-to-end inference workload analysis to improve latency, throughput, scalability, resource utilization, and cost efficiency, while building proof-of-concepts and reference architectures. Collaborate with cloud providers and NVIDIA engineering, product, sales, and business development to translate customer requirements into scalable solutions and product feedback.
Location: Beijing, Shenzhen
Workplace: Onsite
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering

Key Responsibilities

  • •Work with customers to design, deploy, and optimize large-scale LLM and agentic AI inference systems on NVIDIA accelerated computing platforms.
  • •Analyze end-to-end inference workloads and identify opportunities to improve latency, throughput, scalability, resource utilization, and cost efficiency.
  • •Explore and develop solutions for emerging agentic AI inference challenges such as KV cache management, intelligent routing/scheduling, distributed or disaggregated serving, heterogeneous inference, and large-scale model parallelism.
  • •Build proof-of-concepts, performance studies, and reference solutions to validate new architectures and demonstrate NVIDIA technology value in real-world workloads.
  • •Partner with NVIDIA Engineering, Product, Sales, and Business Development to solve customer problems, provide product feedback, and help shape technical strategies for AI infrastructure opportunities.

Key Requirements

  • •MS or PhD in Electrical Engineering, Computer Science/Engineering, Mathematics, Physics, or a related field (or equivalent practical experience).
  • •3+ years of experience in large language models, AI inference, distributed AI systems, or related areas, with hands-on system design and performance optimization for large-scale AI workloads.
  • •Strong understanding of modern AI systems, including analyzing performance across models, software frameworks, compute, memory, communication, and infrastructure.
  • •Strong programming and problem-solving skills, including experience using common AI frameworks and tools; proficiency with AI tools/agents to improve engineering productivity is valued.
  • •Clear technical communication and structured thinking, able to work effectively with engineering teams and senior technical stakeholders.
Experience:3+ yearsAgentic AILarge language modelsAI inferenceDistributed AI systemsGPU-accelerated systemsProduction AI infrastructure
Education:Master's
Skills:Technical communicationStructured thinkingProblem-solving
Tech Stack:Agentic AILLMKV cache managementIntelligent routingSchedulingDisaggregated servingHeterogeneous inferenceLarge-scale model parallelismNVIDIA DynamoTensorRT-LLMExpert parallelism

Company Brief

NVIDIA
Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.
Industry: Electronics Manufacturing
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1993
Glassdoor
Glassdoor: 4.3
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