Required Qualifications

5+ years of experience architecting, implementing, selling, or marketing enterprise storage and data platform solutions, with a strong focus on AI/ML infrastructure, including:

Storage & Data Infrastructure

• High-performance parallel file systems: VAST Data Platform, Weka Data Platform, DDN EXAScaler / AI400, IBM GPFS / Spectrum Scale, Lustre

• Enterprise NAS and object storage: NetApp ONTAP AI, StorageGRID, Dell PowerScale (Isilon), Dell ECS / ObjectScale, Dell PowerStore

• Storage sizing and performance modeling for AI training, checkpointing, and inference workloads

NVIDIA AI Data Platform

• Familiarity with NVIDIA AI Data Platform design requirements, reference architectures, and validation frameworks

• Experience with NVIDIA GPU-Direct Storage, NVAIE, Mission Control, and related software ecosystem components

• Understanding of how storage and data platforms integrate with NVIDIA DGX, HGX, MGX, and OVX compute architectures

Data Pipelines & Frameworks

• Data pipeline design and orchestration using Apache Spark, Ray, Dask, Apache Airflow, and/or Prefect

• Data lakehouse and data fabric concepts: Delta Lake, Apache Iceberg, Apache Hudi, Unity Catalog

• Data ingestion, transformation, and movement tools (e.g., Apache Kafka, Apache NiFi, Airbyte)

Professional Skills

• Demonstrated ability to engage executive and technical audiences with equal fluency

• Strong written and verbal communication skills; experience delivering architecture presentations and technical proposals

• Sound organizational, conflict resolution, time management, and negotiation skills

Nice to Have

• Python scripting for data pipeline automation or storage performance testing

• Experience with NVIDIA Run:ai, Slurm, or other workload schedulers as they relate to storage I/O optimization

• Previous exposure to NVIDIA Enterprise and NCP reference architectures

• Experience with cloud storage integration: AWS S3, Azure Blob, Google Cloud Storage, and hybrid data fabric designs

• Familiarity with data governance, data cataloging, and compliance frameworks in AI environments

Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $125,000.00 to $156,000.00 annually. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that is not included in the base pay.

The well-being of WWT employees is essential. So, when it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full-time employees:

• Health and Wellbeing: Health, Dental, and Vision Care, Onsite Health Centers, Employee Assistance Program, Wellness program
• Financial Benefits: Competitive pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Tuition Reimbursement
• Paid Time Off: PTO and Sick Leave (starting at 20 days per year) & Holidays (10 per year), Parental Leave, Military Leave, Bereavement
• Additional Perks: Nursing Mothers Benefits, Voluntary Legal, Pet Insurance, Employee Discount Program

If you have any questions or concerns about this posting, please email [email protected].

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What will you be doing?

World Wide Technology is seeking a Technical Solutions Architect (TSA) - AI Storage & Data Platforms to join our AI & High-Performance Architectures (HPA) practice within the Global Solutions & Architecture (GS&A) team. This is a strategic, customer-facing role focused on architecting and delivering high-performance storage, data platform, and data pipeline solutions that power AI and machine learning workloads at scale.

You will serve as WWT's subject matter expert on AI data infrastructure - spanning the full stack from raw storage and data movement to data frameworks, lakehouse architectures, and NVIDIA AI Data Platform design - working alongside our world-class partner ecosystem including NetApp, VAST Data, Weka, DDN, Dell, and Everpure.

What You'll Own

AI Data Platform Architecture Lead the design and sizing of end-to-end AI data infrastructure - from GPU-Direct Storage and high-performance parallel file systems to data lakehouse and data fabric architectures - aligned to NVIDIA AI Data Platform design requirements and partner reference architectures.

Partner Solution Development Serve as WWT's technical lead for our storage and data platform partner ecosystem, including NetApp, VAST Data, Weka, DDN, Dell (PowerStore, PowerScale, ECS, ObjectScale), and Everpure. Develop and maintain joint solution designs, reference architectures, and go-to-market plays.

Pre-Sales & Customer Engagement Engage directly with customers to assess AI data infrastructure requirements, define architectures, and deliver compelling solution proposals - including HLDs, BOMs, and LLDs - for enterprise and hyperscale AI workloads.

Field Enablement & Thought Leadership Develop and deliver training, briefings, workshops, and reference architectures that enable WWT's field teams and partners to position and sell AI storage and data platform solutions with confidence.

Pipeline & Business Development Collaborate with regional architects, sales leadership, and the broader GS&A practice to identify, qualify, and advance new business opportunities in the AI data infrastructure space.

Practice & Partner Alignment Maintain OEM and partner certifications, track product roadmaps, and align WWT's go-to-market strategy with partner initiatives - including NVIDIA AI Data Platform certifications and NetApp, VAST, Weka, and DDN partner programs.

Responsibilities

• Architect high-performance storage and data platform solutions for AI/ML workloads, aligned to NVIDIA AI Data Platform design patterns and partner-validated reference architectures

• Design and size parallel file system, object storage, NVMe-oF, and GPU-Direct Storage (GDS) environments based on model type, dataset scale, and customer use case

• Develop data pipeline and data framework architectures supporting AI training, inference, and MLOps workflows - leveraging tools such as Apache Spark, Ray, Dask, Airflow, MLflow, and Kubeflow

• Define data lakehouse and data fabric architectures using platforms such as NetApp ONTAP AI, VAST Data Platform, Weka Data Platform, DDN EXAScaler/AI400, and Dell PowerScale/ECS

• Collaborate with HPA compute and networking architects to deliver integrated AI Factory designs across compute, storage, networking, and data layers

• Assist customers with GPU-Direct Storage adoption, NVMe-oF fabric design, and data movement optimization for GPU-accelerated training and inference

• Deliver market scans, competitive analysis, and solution comparisons across WWT's AI storage and data platform portfolio

• Support field teams with deal-level technical assistance including architecture reviews, sizing guidance, and customer presentations

• Drive partner roadmap alignment and co-sell motions with NetApp, VAST Data, Weka, DDN, Dell, Everpure, and NVIDIA

• Develop enablement materials including brochures, briefings, workshops, reference architectures, and demos

• Participate in regular HPA team meetings, partner briefings, and training sessions

• Travel as required to customer sites, partner events, and WWT facilities

Travel Requirements: 25-50%