Top Solutions Architect Jobs

Leads end-to-end solution architecture for a complex federal program, coordinating engineering, networking, cloud, cybersecurity, operations, testing, and service delivery teams. Defines architecture frameworks, standards, roadmaps, and design patterns; evaluates technical alternatives; leads architecture reviews; manages technical risks; and ensures scalability, resiliency, security, maintainability, and performance. Supports requirements analysis, system decomposition, transition planning, operational readiness, and executive-level technical briefings. The role is onsite in Chantilly, Virginia, and requires obtaining and maintaining a customer-specific government clearance.

Leads presales architecture for enterprise data and AI solutions, focusing on on-premises data platforms, data warehouses, integration, governance, security, modernization, and disaster recovery. Develops technical proposals, cost models, ROI and TCO analyses, migration roadmaps, and implementation plans. Partners with sales, delivery, engineering, finance, and customers to connect data architecture to business value and ensure successful post-sales transition.

Serve as the founding customer-facing engineer for Kimchi, leading demos, customer environment setup, LLM inference optimization, model routing, governance policy implementation, and serverless API integrations. Translate customer feedback into product improvements while identifying revenue expansion opportunities. Explain complex AI concepts to non-ML engineers and optimize workloads for coding, reasoning, and planning use cases.

Design and scale Kubernetes-native, GPU-accelerated cloud architectures for large-scale robotics simulation, synthetic data pipelines, training, and inference. Advise partners, optimize scheduling, storage, networking, and GPU utilization, and drive adoption of NVIDIA Physical AI frameworks while collaborating with product and engineering teams.

Lead Day 2 operations engagements with NVIDIA Cloud Partners: diagnose and fix production GPU/HPC/cloud issues, prototype and validate fixes, improve reliability, performance, and economics, and convert solutions into reusable procedures, automation, and feedback for product and engineering teams.

Partner with industry and account teams to design and deliver NVIDIA-based AI, HPC, and GPU/networking solutions for industrial robotics. Engage customers and partners, produce technical documentation and samples, support deployments both in datacenters and at the edge, and lead technical engagements with lighthouse customers and solution partners.

Lead Day 2 operations improvements across NVIDIA Cloud Partners operating GPU, HPC, and AI cloud infrastructure. Partner with engineering teams to troubleshoot production issues, improve reliability, performance, utilization, recovery, and economics, and prepare new NVIDIA technologies for operations. Raise operational maturity across tooling, telemetry, security, incident response, and automation. Convert validated solutions into procedures, architectures, assessments, and workflows while providing cross-partner feedback to NVIDIA engineering and product teams.

Design and deploy scalable AI inference architectures for strategic partners. Build and operate GPU inference recipes, optimize pipelines using leading inference backends, and guide disaggregated serving deployments. Evangelize Kubernetes, compute fabric, DevOps, and observability practices while mentoring customers and internal teams. Lead technical engagements, resolve complex infrastructure issues, create reference architectures, and provide product insights. The role also involves agent design, sandboxing, memory and retrieval systems, evaluation, skill governance, and model customization.

Support NVIDIA networking technologies (Spectrum-X, RoCE, InfiniBand); engage customers in pre-sales, develop proofs-of-concept, debug sophisticated issues, collaborate with R&D on features, and help define product requirements for AI/HPC networking platforms.

Designs and deploys large-scale AI and HPC GPU cloud infrastructure for NVIDIA Cloud Partners. Serves as the primary technical advisor across solution design, development, integration, production, debugging, and customer adoption. Builds proof-of-concepts, integrates AI software stacks, libraries, frameworks, and models, and delivers technical presentations and workshops. Partners with engineering, product, sales, and business teams to secure opportunities, guide product strategy, and support complex customer engagements throughout the lifecycle.

Build and scale robotics simulation and sim-to-real workflows for NVIDIA partners. Develop solutions involving robot rigging, physics, rendering, sensor simulation, synthetic data, GPU computing, and learned policies. Lead technical evaluations, proofs of concept, workshops, and reference implementations; diagnose performance and integration issues; validate hybrid physics and neural approaches; and collaborate with research, engineering, product, and customers to guide platform adoption and roadmaps.

Leads the design, development, integration, sizing, costing, documentation, and governance of enterprise cloud solutions for external clients. Translates business and technical needs into validated requirements, aligns technical solutions with commercial engagements, manages architecture projects and teams, defines standards, resolves complex issues, and presents solutions to stakeholders. The role focuses on hybrid and multicloud modernization, enterprise implementation, and trusted technical advising.

Lead technical engagements with OEM and deployment partners to design, validate, and deploy scalable NVIDIA AI infrastructure. Map customer workloads to NVIDIA hardware and software, co-design architectures, resolve complex technical issues, drive platform adoption, and influence product priorities. Lead AI Factory build-readiness execution, deployment validation, technical enablement, and issue resolution across large-scale GPU infrastructure deployments. Travel up to 50% is required.

Designs and optimizes NVIDIA AI and HPC infrastructure for energy-sector customers, including GPU clusters, networking, storage, orchestration, monitoring, security, and edge deployments. Partners with customers and internal teams on architecture reviews, proof-of-concepts, benchmarks, production deployments, reference architectures, and technical documentation. Advises on distributed training, inference, RAG, cluster reliability, and performance optimization while supporting strategic customer and partner engagements.

Drive technical engagements for NVIDIA’s on-premises AI Factory solutions. Advise partners, OEMs, and enterprise customers; design and validate scalable architectures; support deployments and troubleshooting; enable adoption of NVIDIA platforms; and communicate customer needs to engineering and product teams. The role requires hands-on expertise across AI infrastructure, hardware, firmware, operating systems, cluster management, workload orchestration, and system integration.

Senior Solutions Architect responsible for designing, deploying, and supporting large-scale NVIDIA GPU infrastructure for cloud partners. The role leads customer technical engagements across the full lifecycle, develops proofs of concept, integrates networking and compute solutions, troubleshoots performance and scaling issues, delivers technical presentations and workshops, and partners with sales and business teams to identify opportunities and inform product strategy. Expertise in AI/HPC data centers, GPU clusters, NVIDIA systems, performance testing, and cluster administration is valued.

Design and architect large-scale GPU clusters for AI training, inference, and HPC workloads. Collaborate with customers, OEMs, product teams, and engineering on compute, networking, storage, topologies, performance modeling, validation, debugging, and new product deployments. Support enterprise cluster bring-ups and provide technical guidance on NVIDIA GPUs, NVLink, networking architectures, and distributed training technologies.

Design secure, scalable AI and cloud architectures for strategic Microsoft partners. Lead architecture sessions, develop proofs of concept and pilots, provide technical implementation guidance, modernize legacy systems, and help partners build AI practices, reusable intellectual property, agentic solutions, and managed services. Collaborate with partner executives, architects, developers, and Microsoft engineering while integrating AI models with enterprise data, applications, APIs, security, governance, and observability.

Lead performance engineering for AV and robotics workloads: engage customers, develop parallel algorithms and GPU-optimized libraries, profile and tune GPU kernels, optimize distributed transformer communication using NCCL/NVSHMEM and interconnect strategies, and collaborate with architecture and system teams to influence next-generation hardware and software.

Serve as a technical advisor to NVIDIA Cloud Partners, designing and deploying AI and HPC GPU infrastructure. Build generative AI and LLM proof-of-concepts, support customers throughout the infrastructure lifecycle, analyze performance and scaling, collaborate with sales and technical teams, and deliver customer workshops and presentations.

Designs, implements, tests, and validates Ethernet networking solutions for AI-enabled data centers. Provides routing expertise across BGP, VXLAN, EVPN, Linux, CLOS architectures, and switch fabrics; develops automation scripts and deployment documentation; validates configurations in labs; collaborates with customers, engineering, and marketing; and supports network performance monitoring, technical presentations, and training.

Lead integration and deployment of NVIDIA networking for hyperscale AI/HPC customers. Analyze, design, implement, and troubleshoot large-scale networks; develop PoCs; run technical meetings, debugging sessions, and workshops; partner with Sales and Product teams to define requirements and secure opportunities.

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.

Lead GPU and HPC cluster architecture and deployment for cloud partners: design end-to-end GPU/NVLink infrastructures, perform performance modeling, validate and debug cluster configurations, support NPI deployments, and serve as technical liaison between engineering, product, and field teams.

Develops high-performance computing, AI, machine learning, and GPU solutions for customers and partners. Acts as a trusted technical advisor, designs enterprise GPU and networking architectures, supports AI and HPC applications, creates technical documentation and educational content, and collaborates with industry teams, developers, researchers, and senior stakeholders. The role includes customer engagements, partner strategy, solution delivery, and leadership across complex technology initiatives.