ePlus Technology
Principal Solutions Architect (
San Ramon, CA · Principal
Sponsorship not specifiedDetected 22 days ago
Node.jsFull-Stack DevelopmentKubernetesMachine LearningTensorFlowPyTorchData ScienceNLPLLMsMLOpsZero TrustCommunication
Stay score
odds of building a lasting career here
37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
Personalize to your clock →Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- We are seeking an elite Solutions Architect to lead the end-to-end design, sizing, and deployment of NVIDIA AI Factory-aligned infrastructure. In this highly technical, customer-facing role you will translate complex AI and machine learning workload requirements into fully engineered infrastructure solutions spanning colocation facilities, GPU compute, high-performance networking, parallel storage, and the complete NVIDIA AI software stack.
- You will serve as a trusted technical advisor to enterprise and hyperscale customers, partnering with sales, product, and engineering teams to win and deliver transformational AI infrastructure programs. Your expertise will directly shape how organizations build and operate production AI Factories capable of training frontier models, running large-scale inference fleets, and accelerating data science pipelines at scale.
- Your Impact
Responsibilities
- Solution Design & Architecture
- Lead discovery workshops to capture AI/ML workload requirements, including model training scale, inference SLAs, data pipeline throughput, and multi-tenancy needs.
- Develop detailed Bills of Materials (BOMs), rack elevation diagrams, network topology drawings, and power/cooling budgets for customer proposals.
- Design RTX PRO 6000 Blackwell Server Edition deployments for inference-optimized and enterprise AI workloads.
- Conduct workload sizing and TCO/ROI modeling to validate infrastructure dimensioning for training, finetuning, and inference at scale.
- Design high-density GPU deployments utilizing air-cooled, direct liquid cooling (DLC), and rear-door heat exchanger configurations.
- Engage colocation providers and data center operators to validate capacity availability and negotiate technical SLAs.
- Coordinate with facilities and MEP engineers to validate power infrastructure from utility feed through PDU to rack level.
Skills
- Specify colocation requirements including critical power load (MW-scale), UPS and generator configurations, and PUE targets.
- Define meet-me room (MMR) and cross-connect requirements; specify carrier-neutral telecom diversity strategies.
- Define server firmware, BIOS, BMC, and DGXOS baselines for production GPU infrastructure.
- Establish GPU health monitoring, RAS (Reliability, Availability, Serviceability) policies, and lifecycle management procedures.
- Architect Spectrum-X Ethernet-based AI networking solutions for inference clusters requiring highbandwidth, low-latency connectivity.
- Define Quality of Service (QoS) policies separating storage, compute fabric, and management plane traffic.
- Size storage capacity, IOPS, and throughput based on dataset characteristics, checkpoint frequency, and concurrent reader/writer counts.
- Configure VAST Data Universal Storage for disaggregated storage with NFS, S3, and POSIX access; tune for large sequential read performance.
Apply directly at ePlus Technology →Create a free account for alerts like thisView ePlus Technology immigration profile
This listing is sourced directly from ePlus Technology's careers page and normalized into a canonical job model.