Solutions Architect - AI Factory

NVIDIA
Seoul
Workplace: OnsiteFull timeFunction: Solutions Engineering & Sales EngineeringExperience: 7+ yearsEducation: bachelorsSkills: ["Technical advisory","Customer collaboration","Problem-solving"]

Serve as a trusted technical advisor to help customers implement AI training and inference clusters using NVIDIA’s AI Factory reference architectures. You’ll analyze customer requirements, select the best-fit architecture, and coordinate communication between internal deployment teams and customer collaborators. After deployment, provide hands-on support to ensure Kubernetes-based workloads run effectively on NVIDIA GPU infrastructure, including networking and management fabrics.

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FursaFursa
NVIDIA
NVIDIA
1 month ago

Solutions Architect - AI Factory

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Source: Company careers pageValidated by: Fursa AI
Last checked: 4 hours agoStatus: Live

Job Summary

Serve as a trusted technical advisor to help customers implement AI training and inference clusters using NVIDIA’s AI Factory reference architectures. You’ll analyze customer requirements, select the best-fit architecture, and coordinate communication between internal deployment teams and customer collaborators. After deployment, provide hands-on support to ensure Kubernetes-based workloads run effectively on NVIDIA GPU infrastructure, including networking and management fabrics.
Location: Seoul
Workplace: Onsite
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering
Seniority: Mid level

Key Responsibilities

  • •Maintain up-to-date understanding of NVIDIA reference architectures and deployment methods.
  • •Analyze customer requirements for AI training and inference clusters and identify the best-matching reference architecture.
  • •Communicate the value proposition of selected reference architectures to collaborators.
  • •Facilitate seamless communication between NVIDIA internal deployment teams and customers during implementation.
  • •Provide hands-on technical support post-deployment to ensure AI workloads run effectively on the infrastructure.

Key Requirements

  • •Bachelor’s degree or higher in Computer Science, Computer Engineering, or a related technical field.
  • •Minimum 7 years of hands-on experience designing, deploying, and operating AI training or inference clusters using Kubernetes with NVIDIA GPUs.
  • •Hands-on experience with server, storage, and network for AI cluster environments.
  • •Foundational networking knowledge in AI clusters, including InfiniBand and Ethernet, plus compute/storage interconnects and management fabrics.
  • •Proficiency with technologies such as CRI, CNI, Calico, NVIDIA GPU Operator, NVIDIA Network Operator, and Kubeflow Training Operator.
Experience:7+ yearsAI trainingAI inferenceDeep learningAccelerated computingHigh-performance computingKubernetes
Education:Bachelor's in Computer Science or Computer Engineering
Skills:Technical advisoryCustomer collaborationProblem-solving
Tech Stack:KubernetesNVIDIA GPUsContainer Runtime Interface (CRI)Container Network Interface (CNI)CalicoNVIDIA GPU OperatorNVIDIA Network OperatorKubeflow Training OperatorInfiniBandEthernetDGX SuperPOD Reference ArchitectureCloud Partner Reference DesignEnterprise Reference Architecture

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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