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About the Role

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.

Responsibilities

Lead architecture discussions starting from data characteristics and lifecycle, including data volume, velocity, and distribution, structured vs. unstructured data considerations, and data locality, gravity, and movement patterns.
Design AI solutions by optimizing data access patterns (sequential vs. random, batch vs. real-time) and AI pipelines for data movement efficiency, minimizing bottlenecks between storage, compute, and model layers.
Optimize 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.
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.
Scope and size AI Factory environments based on GPU counts and configurations, data volumes and throughput requirements, and model types and workloads (RAG, inference, training).
Define performance expectations across the full AI pipeline, including data ingestion and preparation, storage and retrieval patterns, and GPU utilization and efficiency.
Provide guidance on optimizing time-to-first-token (TTFT), throughput, and cost efficiency.
Articulate the role of modern data platforms in AI workflows, including object storage (S3) architectures, data pipelines and pipeline simplification/elimination strategies, and 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.
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.

Requirements

8+ years of experience in a technical presales, solutions architecture, or field architect role.
Strong understanding of AI/ML workflows, including Retrieval-Augmented Generation (RAG) and model inference and deployment.
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 and 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, and 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, and 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.
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.

Skills

AI/ML workflows
Retrieval-Augmented Generation (RAG)
Model inference and deployment
Model training pipelines
Data-centric AI architecture
Data volume, velocity, and distribution
Structured vs. unstructured data
Data locality, gravity, and movement
Data access patterns
AI pipelines
Metadata, indexing, and retrieval
Performance optimization
Cost efficiency
Reliability
Trustworthiness
Model performance
Latency
Time-to-first-token
GPU utilization
Customer engagement
Deal leadership
Technical discovery
AI Factory opportunities
Business objectives translation
Scalable AI architectures
Solution design
Trusted advisor
CTO engagement
Head of AI engagement
Data Engineering leader engagement
Deal progression
Business outcomes alignment
Workload requirements
Scale assessment
AI Solution Architecture
Sizing AI environments
GPU configurations
Data volumes
Throughput requirements
Model types
Performance expectations
Data ingestion
Data preparation
Storage and retrieval patterns
Data & Storage Integration
Modern data platforms
AI workflows
Object storage (S3)
Data pipelines
Vector databases
AI frameworks
Cross-Functional Leadership
Product Management collaboration
Roadmap influence
Field feedback
Customer requirements analysis
Competitive dynamics analysis
Technical content creation
Reference architectures
Design patterns
Whitepapers
Conference talks
Internal/external publications
High-performance computing (HPC)
Distributed compute environments
AI/ML frameworks
PyTorch
TensorFlow
Vector databases
Cloud AI infrastructure
Hybrid AI infrastructure
Storage technologies
Object storage
High-throughput data platforms
Product strategy influence
Sales enablement

Location

Hybrid

Work Type

Hybrid

Experience Level

8+ years of experience

Benefits

Health & Wellbeing comprehensive suite of benefits
Personal & Professional Development programs
Unconditional Inclusion

About the Company

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.

Equal Opportunity

HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities.
HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer.
We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need.
Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together.
Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.
HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.