Senior Solution Architect, MLOps - AI Factory

NVIDIA is hiring a Senior Solution Architect, MLOps - AI Factory for a remote role open to applicants in Germany. Level rates it ; you can apply on Level.

AI in this role

Architect and scale high-performance distributed AI infrastructure and MLOps pipelines using NVIDIA GPUs.

vllmpytorchkubernetestritonserverpython

ml-opsmlopsdeep-learningdistributed-systemsinfrastructuregpu-computing

We are looking for an AI Factory Solution Architect. NVIDIA is at the forefront of the AI computing revolution, building innovative deep learning solutions that reshape industries worldwide. In this role, you will play a key role in introducing our advanced GPU products to deployments across data centers. If you enjoy system building and have a demonstrable track record of technical customer interactions, this is a prime opportunity to make a meaningful contribution.

What You'll Be Doing:

Help architect and scale high-performance, distributed AI infrastructure on-prem or in the cloud, built with the latest NVIDIA GPU supercomputers for new and existing customers.
Be a motivating leader in integrating NVIDIA technology into HPC architectures to support scientific and engineering applications.
Be an internal champion for Deep Learning and Infrastructure among 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.

What We Need To See:

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 the 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.
Capable of working in a constantly evolving environment without losing focus.
Committed with 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 very complex projects.

Ways To Stand Out From The Crowd:

Technical knowledge of developer digital platforms and their trends.
Experience working with Nvidia operators.
Expertise in operating Kubernetes, as well as experience writing or customizing Kubernetes configurations.
Background in deploying large-scale multi-node training and inferencing pipelines.
Good presentation skills

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking individuals in the world working for us. If you're creative and autonomous, we want to hear from you.

How we rate this

Senior Solution Architect, MLOps - AI Factory at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Senior Solution Architect, MLOps - AI Factory is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Germany, Remote and is marked remote.
The job was posted on October 5, 2026.
Seniority: senior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsMLOpsDeep learningDistributed SystemsInfrastructureGPU ComputingvLLMPyTorch

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Tell me about a project where mlops was part of your work. What did you do?
Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where distributed systems was part of your work. What did you do?
Tell me about a project where infrastructure was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: ML Ops, MLOps, Deep learning, Distributed Systems, and Infrastructure. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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