Principal Solutions Architect

NVIDIA

Principal Solutions Architect helping NVIDIA Cloud Partners deploy large-scale AI and GPU infrastructure. Integrating NVIDIA hardware, software, models, and cloud architectures into production.

Posted 9/2/2026full-timeRemote • Maryland • 🇺🇸 United StatesLead💰 $272,000 - $431,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in Solution Engineering, particularly in designing and deploying large-scale Cloud architectures and GPU infrastructure. Proficient in programming with Python and Deep Learning frameworks, with a strong focus on MLOps and customer engagement throughout the project lifecycle.

Highest-signal resume keywords

Solution EngineeringLarge-Scale Cloud ArchitecturePython ProgrammingDeep Learning FrameworksNVIDIA GPU Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Solution EngineeringLarge-Scale Cluster EnvironmentsDistributed Cloud ArchitecturesDeep Learning FrameworksModel Fine TuningMLOpsPerformance TestingAI BenchmarkingCluster OrchestrationCloud Architecture

Soft Skills

Excellent Presentation SkillsCommunication SkillsCollaboration SkillsTime ManagementSelf-Starter

Tools & Technologies

NVIDIA GPUsNVIDIA NeMo FrameworkNVIDIA Triton Inference ServerTensorRTAWSAzureGCPNCCLDCGMSLURM

Industry Keywords

Cloud EngineeringSolution ArchitectureAIMLGPU Cloud Infrastructure

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetesPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Collaborate with NVIDIA Cloud Partners to create, implement, and deliver NVIDIA's innovative hardware and software solutions
Partner with SAs, Account Managers, Engineering, Product, and business leaders to align on strategies, assess technical needs, and secure business opportunities for NVIDIA
Become the primary technical driver for customers during the design, development, construction, integration, and production of GPU Cloud infrastructure and applications throughout the entire customer lifecycle
Conduct regular technical customer meetings for project/product details, feature discussions, introductions to new technologies, and debugging sessions
Work closely with customers to build and adopt NVIDIA solutions, including PoCs, to address critical business needs covering infrastructure, libraries, and applications
Prepare and deliver technical content to customers, including presentations, workshops, reference architectures, tutorials, and publications
Drive outtake and consumption by integrating libraries, frameworks, models, and software applications
Deliver GenAI, AI, and ML hardware/software to production with consequential customers and partners
Own end-to-end technology solution integration with strategic customers and offer product strategy recommendations based on feedback

Requirements

What you’ll need

BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, Mathematics, or other Engineering fields or equivalent experience
15+ years of Solution Engineering (or similar Sales Engineering, Cloud Engineering, Solution Architecture) including experience working directly with partners and customers
Experience crafting and deploying large-scale cluster environments
Hands-on experience designing, developing, delivering distributed Cloud architectures
Strong fundamentals in programming, optimizations and software design, especially in Python and Deep Learning frameworks such as PyTorch and TensorFlow
Practical expertise fine tuning and deploying models, integrating software application stacks, libraries, and frameworks to drive consumption from GPU platforms
Motivation and skills to own and drive complex multi-disciplinary technical engagements with customers throughout the full customer lifecycle and cross-functional teams
Efficient time management and capable of balancing multiple tasks
Excellent presentation, communication and collaboration skills
Self-starter with a passion for growth, continuous learning, and sharing insights
Practical experience with NVIDIA GPUs, software libraries, frameworks, and foundation models, such as NVIDIA Nemotron, NVIDIA NeMo Framework, NVIDIA Dynamo, NeMo Retriever, NVIDIA Triton Inference Server, TensorRT, TensorRT-LLM, NVIDIA CUDA-X
Hands-on expertise with scaled AI cloud environments (e.g., AWS, Azure, GCP) and on-premises / hybrid infrastructure, in particular inference and training workloads
Familiarity with NVIDIA hardware (such as GPUs, networking, storage) and systems technology such as NCCL, DCGM, UFM, Mission Control, Base Command Manager
Proficiency with large-scale AI model training / deployment encompassing GPU systems, performance testing, AI benchmarking, fine tuning, strong focus on MLOps and cluster orchestration (SLURM, K8s, orchestrator, load balancing, cloud architecture)
Experience working with enterprise developers and strong customer-facing skills

Benefits

Comp & perks

Equity
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