Senior Solutions Architect, AI and HPC Infrastructure

NVIDIA

. Partner with energy-sector customers, ISVs, and account teams on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance .

Posted 10/21/2026full-timeRemote • Texas • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing AI infrastructure, including large-scale GPU clusters and accelerated computing environments. Proficient in deploying and managing distributed systems, cloud infrastructure, and machine learning platforms while effectively collaborating with energy-sector customers.

Highest-signal resume keywords

AI Infrastructure DesignAccelerated Computing DeploymentCluster OrchestrationData Center NetworkingMachine Learning Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Cluster OrchestrationDistributed SystemsHigh-Performance ComputingCI/CD Deployment PipelinesGPU Communication PatternsDockerKubernetesMLPerf EvaluationPerformance TuningModel Deployment

Soft Skills

Excellent CommunicationProblem-SolvingCollaborationDocumentationPresentation Skills

Tools & Technologies

OpenMPINCCLTelemetry ToolsMonitoring ToolsAI Factories

Industry Keywords

Energy SectorData Center OperationsDistributed TrainingInference ServingOperational IT Environments

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerKubernetesNode.js

About the role

Key responsibilities & impact

Partner with energy-sector customers, ISVs, and account teams on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance
Advise on the design, build-out, and optimization of accelerated AI infrastructure, including large-scale GPU clusters, AI factories, and edge deployments
Support infrastructure design across compute, networking, storage, containers, observability, security, power, and data center operations
Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization, reliability, performance, and workload insight
Document learnings and teach others through samples, whitepapers, blogs, and customer problem-solving
Build reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos, and whitepapers
Partner strategically with flagship customers and industry-specific solution partners
Collaborate primarily through virtual tools
Travel up to 20% for customer meetings may be required

Requirements

What you’ll need

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, other Engineering, or related field (or equivalent experience)
8+ years of hands-on experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, high-performance computing, or machine learning platforms
Strong experience designing, deploying, and operating accelerated computing infrastructure at scale
In-depth knowledge of AI cluster orchestration, scheduling, automation, and CI/CD deployment pipelines
Understanding of data center networking technologies such as InfiniBand, Ethernet, RDMA, network configuration, and performance tuning
Familiarity with distributed training, inference serving, model deployment, storage performance, and cluster reliability requirements
Working knowledge of one or more energy sectors and their operational and IT environments
Experience architecting AI factories, large GPU clusters, multi-node training environments, or production inference platforms
Experience deploying LLM training, fine-tuning, RAG, and inference workflows on large-scale AI infrastructure
Experience evaluating cluster performance using MLPerf, HPL, or workload-specific performance tests
Applications and systems-level knowledge of OpenMPI, NCCL, distributed training frameworks, and GPU communication patterns
Comfortable with Docker/Containers and Kubernetes
Excellent presentation, communication, problem-solving, documentation, and collaboration skills
Strong problem structuring and self-direction to drive ambiguous, multi-stakeholder projects to results

Benefits

Comp & perks

Equity
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