AI Data Architect

About the role

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.

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.

Requirements

• 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