AI Data Platform Field Architect

About the role

Hewlett Packard Enterprise is seeking an AI Data Platform Field Architect for its X10K AI data platform business in Seoul. This is a customer-facing, data-first architect role that works on average 2 days per week from an HPE office. It bridges data architecture, AI infrastructure and business value, partnering with customers and sales teams on RAG, inference and model training solutions, and acting as a bridge between the field and Product Management.

Key responsibilities

• Lead architecture discussions starting from data characteristics and lifecycle, covering data volume, velocity, distribution, structured vs. unstructured data, locality and movement patterns
• Optimize data access patterns, AI pipelines, metadata, indexing and retrieval efficiency, and recommend design improvements for performance, cost, reliability and trustworthiness
• Evaluate how data design affects model performance, latency, GPU utilization and cost
• Lead technical discovery with enterprise customers, qualify AI Factory opportunities and serve as a trusted advisor to CTOs, Heads of AI and Data Engineering leaders
• Size AI Factory environments by GPU count, data volumes, throughput and model types, and guide optimization of time-to-first-token, throughput and cost
• Explain the role of modern data platforms in AI workflows, including object storage (S3), data pipelines, integration with vector databases and AI frameworks
• Work with Product Management to influence roadmap priorities, gather field feedback and create technical content such as reference architectures, design patterns, whitepapers and conference talks

Required profile

• 8+ years of experience in technical presales, solutions architecture or field architect roles
• Ability to lead architecture from data requirements and access patterns rather than infrastructure-first design
• Proven experience working with Product Management and solution teams to define and extend AI Factory offerings, including reference architectures and roadmap input
• Experience sizing and designing GPU-based environments for AI workloads
• Strong communication skills with technical and executive audiences

Required skills

• Strong understanding of AI/ML workflows, including RAG, model inference and deployment (model training pipelines nice to have)
• Data architecture: data pipelines, data lakes, object storage, performance of large-scale data access
• Nice to have: exposure to HPC or distributed compute, familiarity with PyTorch, TensorFlow and vector databases, cloud and hybrid AI infrastructure, storage technologies, and collaboration with Product Management