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AI Data Platform Field Architect This role has been designated as 'Remote/Teleworker', which means you will primarily work from home.

Who We Are:

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today's complex world. Our culture thrives on finding new and better ways to accelerate what's next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. We are seeking a highly strategic and technically grounded AI Data Platform Field CTO to help drive the next phase of growth for our X10K AI data platform business. This is a customer-facing architect role that leads with a data-first perspective, focusing on how data is created, moved, enriched, and consumed across AI pipelines, using infrastructure as an enabler rather than the starting point. You will operate at the intersection of data architecture, AI infrastructure, and business value, partnering with customers and sales teams to design high-impact AI solutions spanning RAG, inference, and model training workflows. You will also act as a critical bridge between the field and Product Management, influencing roadmap priorities and helping build repeatable, scalable go-to-market motions. A key aspect of this role is the ability to work closely with sales and technical teams to identify and prioritize the right opportunities at the right time as we accelerate adoption in a rapidly evolving market. This includes applying strong technical and commercial judgment to align solutions with customer readiness, workload requirements, and scale, ensuring we win where we can deliver the most impact and long-term success. This is not a pure storage role, but a strong understanding of how data platforms and storage enable AI pipelines is essential, along with the ability to position solutions thoughtfully based on where they deliver the most value. Key Responsibilities Data-Centric AI Architecture

• Lead architecture discussions starting from data characteristics and lifecycle, including:

+ Data volume, velocity, and distribution

+ Structured vs. unstructured data considerations

+ Data locality, gravity, and movement patterns

• Design AI solutions by optimizing:

+ Data access patterns (sequential vs. random, batch vs. real-time)

+ AI pipelines for data movement efficiency, minimizing bottlenecks between storage, compute, and model layers Metadata, indexing, and retrieval efficiency (critical for RAG)

+ Recommend design optimizations and ...