Senior Solution Architect, MLOps – AI Factory

NVIDIA

. Architect and scale high-performance, distributed AI infrastructure on-premises or in the cloud using NVIDIA GPU supercomputers .

Posted 10/5/2026full-timeRemote • Germany, Czechia, France, HungarySeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and scaling high-performance AI infrastructure using NVIDIA GPU supercomputers, with a strong background in Deep Learning frameworks and MLOps tools. Proven ability to solve complex problems within customer infrastructure while effectively managing multiple projects and technical customer interactions.

Highest-signal resume keywords

NVIDIA GPU SupercomputersKubernetes (K8S) Infrastructure OrchestrationDeep Learning Frameworks (PyTorch, vLLM, TritonServer)MLOps ToolsLarge-Scale Multi-Node Training and Inferencing Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software DevelopmentMachine Learning EngineeringProblem SolvingKubernetes ConfigurationDeep Learning Software Architecture

Soft Skills

Analytical SkillsTime ManagementOrganization SkillsPresentation Skills

Tools & Technologies

NVIDIA OperatorsContainersAI Ecosystem

Certifications & Qualifications

B.Sc. or M.Sc. in Engineering, Mathematics, Physics, or Computer Science

Industry Keywords

High-Performance Computing (HPC)Cloud InfrastructureData-Center DeploymentsTechnical Customer Interactions

Tech Stack

Tools & technologies

CloudKubernetesNode.jsPyTorch

About the role

Key responsibilities & impact

Architect and scale high-performance, distributed AI infrastructure on-premises or in the cloud using NVIDIA GPU supercomputers
Integrate NVIDIA technology into HPC architectures supporting scientific and engineering applications
Champion Deep Learning and Infrastructure internally within the NVIDIA technical community
Support development activities and engage in POCs/POVs to validate new features and architectures
Develop solutions and showcase the AI ecosystem
Introduce advanced NVIDIA GPU products to data-center deployments
Conduct technical customer interactions and support customer infrastructure solutions

Requirements

What you’ll need

B.Sc. or M.Sc. in Engineering, Mathematics, Physics, or Computer Science or equivalent experience
5+ years in software development or ML engineering
Extensive ability to solve problems within customer infrastructure
Practical expertise with on-premises Kubernetes (K8S) infrastructure orchestration and platform
Experience working with containers and MLOps tools
Background with modern Deep Learning software architecture and frameworks including PyTorch, vLLM, and TritonServer
Ability to work in a constantly evolving environment without losing focus
Strong analytical and problem-solving skills
Strong time-management and organization skills for coordinating multiple initiatives, priorities, and implementations of new technology and products into complex projects
Technical knowledge of developer digital platforms and their trends
Experience working with NVIDIA operators
Expertise in operating Kubernetes and writing or customizing Kubernetes configurations
Background in deploying large-scale multi-node training and inferencing pipelines
Good presentation skills

Benefits

Comp & perks

NVIDIA is widely considered to be one of the technology world’s most desirable employers
Opportunity to contribute to innovative AI and deep learning solutions
Work with advanced NVIDIA GPU products and technology