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Founded in 1990, WWT brings together strategy, deep technical expertise and world-class

partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)-a collaborative ecosystem featuring state-of-the-art hardware and software-WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distribution capabilities.

With more than 14,000 team members and over 60 locations globally, WWT's culture-grounded in core values and leadership philosophies-has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada.

Want to work with highly motivated individuals on high-performance teams? Join WWT today!

Position Overview

The AI Data Architect is a customer-facing member of the GS&A AI & Data Solutions team who helps organizations design and optimize the data layer that powers modern AI initiatives. This role operates at the intersection of AI, data, and infrastructure, translating business challenges and AI use cases into practical data architectures that improve how enterprise data is discovered, prepared, enriched, governed, indexed, retrieved, and delivered to AI applications.

The architect will help customers evaluate how modern AI data platforms can improve AI readiness, accelerate data pipelines, reduce unnecessary data movement, and make more efficient use of high-value GPU resources. The role requires working knowledge of enterprise storage and the ability to operate within a storage environment, but it is not a storage architect, storage administrator, or product-only specialist position.

This position is funded through Pure Storage and requires meaningful expertise in Pure Storage's implementation and ecosystem. However, the architect must remain outcome-oriented and vendor-aware, applying the broader NVIDIA AI Data Platform architecture across customer environments and understanding how implementations from Pure Storage, NetApp, and other ecosystem partners differ in capability, integration, and customer value.

Role Mission

Help customers transform enterprise data into an AI-ready asset and architect the data pathways that enable scalable, cost-effective AI outcomes. The architect connects business objectives to AI, data, and infrastructure decisions, with particular attention to where data resides, how it moves, what processing occurs before it reaches GPU-accelerated systems, and how the overall design affects latency, utilization, scalability, governance, and cost.

Key Responsibilities

+ Partner with account teams, the HPA team, GS&A AI & Data Solutions architects, and technology partners during pre-sales discovery, workshops, solution design, and customer presentations.

+ Translate customer business challenges and AI use cases into data, platform, integration, security, governance, and infrastructure requirements.

+ Architect the data layer for enterprise AI solutions, including data discovery, preparation, movement, placement, enrichment, metadata, indexing, retrieval, lineage, protection, and lifecycle considerations.

+ Evaluate whether AI performance constraints originate in compute, data availability, data quality, data movement, retrieval design, metadata, networking, or platform architecture.

+ Identify opportunities to reduce unnecessary GPU workload by performing appropriate data preparation, indexing, retrieval, filtering, or other data services outside the primary GPU-intensive AI factory workflow.

+ Explain how data participates in retrieval-augmented generation, agentic AI, inference, model customization, and other AI workflows without serving as the primary data scientist or application developer.

+ Design solution architectures that use NVIDIA AI Data Platform concepts and partner technologies to improve AI pipeline efficiency, data accessibility, application performance, and economics.

+ Develop reusable reference architectures, discovery frameworks, solution patterns, demonstrations, and technical enablement for field teams and customers.

+ Clearly communicate tradeoffs and recommendations to business leaders, AI teams, data engineers, infrastructure teams, security teams, and storage specialists.

+ Maintain strong knowledge of Pure Storage capabilities while objectively evaluating adjacent and alternative approaches across the AI data platform ecosystem.

Core Architectural Focus

+ The successful candidate can reason across the complete AI data path and explain how architectural choices affect outcomes. This includes:

+ Where enterprise data resides and how it becomes accessible to AI applications.

+ What must happen for data to participate in RAG, agentic AI, inference, model training, or model customization workflows.

+ How metadata, catalogs, indexing, embeddings, vector retrieval, governance, and data quality contribute to usable AI context.

+ How data placement and movement affect latency, throughput, scalability, security, GPU utilization, and total cost.

+ Which activities belong near the source data, within a data platform, in an AI application layer, or inside the GPU-accelerated AI factory.

+ How vendor-specific implementations map to broader NVIDIA AI Data Platform intentions and customer requirements.

Qualifications :

+ Experience in data architecture, data engineering, AI architecture, enterprise architecture, infrastructure architecture, or a related customer-facing technical role.

+ Working knowledge of enterprise storage architecture, including the ability to understand and navigate storage platforms, data paths, performance considerations, protection, and lifecycle concepts.

+ Strong conceptual understanding of modern AI solution patterns, including RAG, vector search, agentic systems, inference workflows, AI factories, and GPU-accelerated infrastructure.

+ Ability to explain the role of data within those AI patterns, including preparation, metadata, indexing, retrieval, governance, movement, and delivery.

+ Ability to assess how data architecture decisions influence AI economics and performance, including GPU utilization, latency, scalability, and operational cost.

+ Experience leading technical discovery and translating ambiguous business needs into clear architecture requirements and recommendations.

+ Ability to communicate across business, application, data, AI, infrastructure, security, and partner stakeholders.

+ Ability to learn and app...

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