NVIDIA

1 week ago

Solutions Architect - AI Factory

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 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: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 week ago

Solutions Architect - AI Factory

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

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

Company Brief

NVIDIA

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: 4.3

Website LinkedIn Glassdoor