1 month ago
Seoul, Korea, SouthSenior
Responsibilities
- Lead data-centric architecture discussions covering data volume, velocity, distribution, locality, lifecycle, and access patterns.
- Design and optimize AI pipelines across data ingestion, movement, storage, retrieval, compute, and model interaction layers.
- Lead customer discovery sessions, qualify AI Factory opportunities, and translate business objectives into scalable AI architectures.
- Serve as a trusted technical advisor to CTOs, Heads of AI, and Data Engineering leaders while aligning solutions to measurable business outcomes.
- Scope and size GPU-based AI Factory environments based on GPU configurations, data volumes, throughput, model types, and workloads.
- Define and optimize performance expectations including time-to-first-token, throughput, GPU utilization, latency, cost efficiency, and reliability.
- Position X10K data platforms, object storage, data pipelines, vector database integrations, and AI frameworks for customer-specific requirements.
- Partner with sales and Product Management to progress deals, provide field feedback, influence roadmaps, and develop repeatable go-to-market approaches.
- Create and present reference architectures, design patterns, whitepapers, conference talks, and other technical content.
Requirements
- 8+ years of experience in technical presales, solutions architecture, or field architecture.
- Strong understanding of AI/ML workflows including Retrieval-Augmented Generation, model inference, and deployment; model-training pipeline experience is beneficial.
- Experience leading architecture from data requirements and access patterns and optimizing end-to-end data flow across the AI lifecycle.
- Experience defining, developing, or extending AI Factory offerings, contributing to reference architectures, and influencing product direction.
- Experience sizing and designing GPU-based environments for AI workloads.
- Experience collaborating with AI/ML or data engineering teams on data behavior, access patterns, and model interaction.
- Solid understanding of data pipelines, data lakes, object storage, and large-scale data access performance.
- Ability to match architectures to workload requirements and scale while evaluating performance, cost, complexity, and solution fit.
- Proven ability to lead customer discovery and translate requirements into technical solutions for technical and executive audiences.
- Preferred experience includes HPC or distributed compute, PyTorch, TensorFlow, vector databases, cloud and hybrid AI infrastructure, storage technologies, and Product Management collaboration.
Benefits
- Hybrid work arrangement requiring an average of 2 days per week from an HPE office.
- Comprehensive health, financial, and emotional wellbeing benefits for team members and their loved ones.
- Personal and professional development programs supporting career growth and knowledge expertise.
- Inclusive workplace culture with accessibility support and accommodation options for qualified applicants.
Tech Stack
PyTorchTensorFlow
Categories
Solutions Engineering
About HPE
Hewlett Packard Enterprise (HPE) builds enterprise IT infrastructure and services, including servers, storage, networking (Aruba), and hybrid edge-to-cloud platforms like HPE GreenLake for businesses and public-sector organizations. Formed in 2015 after Hewlett-Packard’s split, HPE is headquartered in Houston, Texas and trades on the NYSE. It sells hardware, software, and subscription-based managed cloud services to help customers run workloads across data centers, colocation, and public clouds.