Senior Solutions Architect, Generative AI Deployment and AIOps

NVIDIA is hiring a Senior Solutions Architect, Generative AI Deployment and AIOps in Santa Clara, United States. It pays $184k-$288k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

Level rates this solutions architect role Builds AI ●●●●: the job is building AI systems.
Pay on this posting starts 9% higher than the typical solutions architect posting on Level.
The posting mentions PyTorch, TensorFlow and MLOps among its tools and skills.
Level classes it as a senior role.
NVIDIA has 2,700+ other open roles on Level.

AI in this role

pytorchtensorflow

ml-ops

NVIDIA is seeking outstanding AI Solutions Architects to assist and support customers that are building solutions with our newest AI technology. At NVIDIA, our solutions architects work across different teams and enjoy helping customers with the latest Accelerated Computing and Deep Learning software and hardware platforms. We're looking to grow our company, and build our teams with the smartest people in the world. Would you like to join us at the forefront of technological advancement? You will become a trusted technical advisor with our customers and work on exciting projects and proof-of-concepts focused on inference for Generative AI and Large Language Models (LLMs). You will also collaborate with a diverse set of internal teams on performance analysis and modeling of inference software. You should be comfortable working in a dynamic environment, and have experience with Generative AI, LLMs and GPU technologies. This role is an excellent opportunity to work in an interdisciplinary team at NVIDIA!

What You Will Be Doing:

Partnering with other solution architects, engineering, product and business teams. Understanding their strategies and technical needs and helping define high-value solutions
Dynamically engaging with developers, scientific researchers, and data scientists, gaining experience across a range of technical areas
Strategically partnering with lighthouse customers and industry-specific solution partners targeting our computing platform
Working closely with customers to help them adopt and build creative solutions using NVIDIA technology and MLOps solutions
Analyzing performance and power efficiency of AI inference workloads on Kubernetes
Some travel to conferences and customers may be required (20%)

What We Need To See:

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience)
8+ years of hands-on experience with Deep Learning frameworks such as PyTorch and TensorFlow
Strong fundamentals in programming, optimizations, and software design, especially in Python
Proficiency in problem-solving and debugging skills in GPU orchestration and Multi-Instance GPU (MIG) management within Kubernetes environments
Experience with containerization and orchestration technologies, monitoring, and observability solutions for AI deployments
Excellent knowledge of the theory and practice of LLM and DL inference
Excellent presentation, communication and collaboration skills

Ways To Stand Out From The Crowd:

Prior experience with DL training at scale, deploying or optimizing DL inference in production
Experience with NVIDIA GPUs and software libraries such as NVIDIA NIM, Dynamo, TensorRT, TensorRT-LLM
Excellent C/C++ programming skills, including debugging, profiling, code optimization, performance analysis, and test design
Familiarity with parallel programming and distributed computing platforms

#NALASAHiring

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 184,000 USD – 287,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

How we rate this

Senior Solutions Architect, Generative AI Deployment and AIOps at NVIDIA rates 87 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. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Solutions Architect, Generative AI Deployment and AIOps is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $184k-$288k a year.
The role is based in US, CA, Santa Clara.
The job was posted on September 16, 2026.
Seniority: senior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsPyTorchTensorFlow

Also common in solutions architect jobs at this level

AWS Fine-tuning Machine learning RAG CUDA AI agents

Pay

This job starts at: $184k
Typical solutions architect job: $169k

This posting starts 9% above the typical solutions architect job. See solutions architect jobs

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: ML Ops, PyTorch, and TensorFlow. 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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