AI Data Platform Field Architect

AI Data Platform Field Architect

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.

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.

Job Description:

Role Overview

We are seeking a highly strategic and technically grounded AI Data Platform Field Architect 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 improvements for performance, cost efficiency, reliability, and trustworthiness.

• Evaluate how data design decisions impact: • Model performance and accuracy
• Latency (including time-to-first-token)
• GPU utilization, ingest requirements, and cost efficiency

Customer Engagement & Deal Leadership

• Lead technical discovery sessions with enterprise customers to identify, shape, and qualify AI Factory opportunities
• Translate business objectives into scalable AI architectures and solution designs
• Serve as a trusted advisor to CTOs, Heads of AI, and Data Engineering leaders
• Drive deal progression by aligning technical solutions to measurable business outcomes
• Apply strong judgment in identifying where solutions are the right fit based on workload, scale, and requirements, ensuring credibility and long-term customer success

AI Solution Architecture & Sizing

• Scope and size AI Factory environments based on: • GPU counts and configurations
• Data volumes and throughput requirements
• Model types and workloads (RAG, inference, training)

• Define performance expectations across the full AI pipeline, including: • Data ingestion and preparation
• Storage and retrieval patterns
• GPU utilization and efficiency

• Provide guidance on optimizing time-to-first-token (TTFT), throughput, and cost efficiency

Data & Storage Integration (X10K Focus)

• Articulate the role of modern data platforms in AI workflows, including: • Object storage (S3) architectures
• Data pipelines and pipeline simplification/elimination strategies
• Integration with vector databases and AI frameworks

• Position data platforms as a strategic enabler of AI performance, not just infrastructure
• Align solution positioning to customer-specific data scale, access patterns, and performance needs

Cross-Functional Leadership

• Partner closely with Product Management to: • Influence roadmap priorities across RAG, inference, and training
• Provide structured field feedback on customer requirements, gaps, and competitive dynamics
• Create and present high‑impact technical content (reference architectures, design patterns, whitepapers, conference talks, and internal/external publications) to influence customers, partners, and internal stakeholders.

Required Qualifications

• 8+ years of experience in a technical presales, solutions architecture, or field arhictect role
• Strong understanding of AI/ML workflows, including: • Retrieval-Augmented Generation (RAG)
• Model inference and deployment
• (Nice to have) Model training pipelines

• Demonstrated ability to lead architecture from data requirements and access patterns rather than infrastructure-first design approaches
• Map and optimize end-to-end data flow across the AI lifecycle, from ingestion through retrieval to model interaction and feedback loops
• Proven experience working with Product Management and solution teams to define, develop, and extend AI Factory offerings, including: • Contributing to reference architectures
• Influencing product direction and roadmap priorities

• Experience sizing and designing GPU-based environments for AI workloads
• Experience working with AI/ML or data engineering teams where data behavior, access patterns, and model interaction—not infrastructure alone—drive architectural decisions
• Solid understanding of data architecture concepts, including: • Data pipelines, data lakes, and object storage
• Performance considerations for large-scale data access

• Demonstrated ability to evaluate and position solutions based on fit for purpose, including: • Matching architectures to workload requirements and scale
• Understanding trade-offs across performance, cost, and complexity

• Proven ability to lead customer discovery and translate requirements into technical solutions
• Strong communication skills with the ability to engage both technical and executive audiences

Preferred (Nice-to-Have) Experience

• Exposure to high-performance computing (HPC) concepts or distributed compute environments
• Familiarity with AI/ML frameworks and ecosystems (e.g., PyTorch, TensorFlow, vector databases)
• Experience working with cloud and hybrid AI infrastructure
• Background in storage technologies (object storage, high-throughput data platforms)
• Experience collaborating with Product Management or influencing product strategy

What Success Looks Like

• Consistently shaping and qualifying high-value AI Factory opportunities
• Positioning solutions with precision—winning in the right opportunities for the right reasons
• Enabling field teams to confidently position and sell AI solutions at scale
• Driving measurable improvements in deal velocity, win rates, and pipeline growth
• Influencing product direction based on real-world customer needs
• Establishing a repeatable, scalable approach to AI Factory solution design

#LI-Hybrid

Accessibility

…