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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Data Architect - **Company:** World Wide Technology - **Location:** Maryland Heights, MO, United States (Remote available) - **Salary:** $150,000.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Application Performance Management, Computing Platforms, Data as a Services, Data Architecture, Data Cleansing, Data Discovery, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Information Retrieval, Metadata, Metadata Repositories, NetApp Applications, Performance Tuning, Search Technologies, Unstructured Data, Enterprise Data Management, Graphics Processing Unit (GPU), Retrieval-Augmented Generation, System Availability, IT Architecture, Indexer, Agentic-AI, Data Layers, Pure Storage, Kubernetes, Low Latency, Data Management, Machine Learning Operations, Data Pipelines, Automation Anywhere - **Published:** October 23, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3466384554&tx=VT6358TYV&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 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 apply vendor-specific platform implementations without becoming limited to a single product or vendor point of view., * Experience with NVIDIA enterprise AI technologies, AI factory concepts, accelerated computing, or AI Data Platform ecosystem solutions. * Experience with Pure Storage platforms and AI-focused data capabilities; experience with NetApp or other enterprise data platform vendors is also valuable. * Familiarity with data pipelines, Kubernetes, MLOps, data catalogs, lakehouse patterns, vector databases, unstructured data platforms, and enterprise data governance. * Experience developing technical workshops, reference architectures, proofs of concept, demonstrations, or customer-facing solution narratives. * Pre-sales consulting experience and the ability to connect technical decisions to measurable business value., WWT will consider for employment, without regard to disability, a disabled applicant who satisfies the requisite skill, experience, education, and other job-related requirements of the job and is capable of performing the essential requirements of the job with or without reasonable accommodation. World Wide Technology is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, sex (including pregnancy), sexual orientation, gender identity, national origin, age, disability, veteran status, genetic information, or other characteristics protected by law. We are committed to working with and providing reasonable accommodations to individuals with disabilities. If you have a disability and you believe you need a reasonable accommodation in order to search for a job opening or to submit an online application, please call 1-800-432-7008 and ask for Human Resources. ## Description 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., * 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.