Remote AI Data Platform Architect — Shape AI Pipelines

Hewlett Packard Enterprise Development LP; North Carolina; On-site; USD 226,000 - 378,000; Full time

Job summary

Hewlett Packard Enterprise Development LP is seeking an AI Data Platform Field Architect to lead data-first AI solution designs for enterprise customers. This is a Remote/Teleworker role with primary work-from-home duties and frequent collaboration with field teams.

You will drive architecture from data requirements and access patterns, scope GPU-based environments, and align solutions with business value across RAG, inference, and training workflows.

Qualifications

• 8+ years in a technical presales, solutions architecture, or field CTO role.
• Strong understanding of AI/ML workflows including RAG, model inference and training pipelines.
• Ability to lead architecture from data requirements and access patterns, not infrastructure-first.
• Experience sizing GPU-based environments for AI workloads.
• Excellent communication with technical and executive audiences.

Responsibilities

• Lead data-first AI architecture discussions with data characteristics and lifecycle.
• Design AI solutions optimizing data access and pipelines.
• Guide performance, cost, reliability, and trust in designs.
• Lead customer discovery sessions and translate requirements into solutions.
• Collaborate with Product Management to influence roadmaps.

Skills

Technical presales

Solutions architecture

Field CTO experience

AI/ML workflows

Data architecture

GPU-based environments

Strong communication

RAG

Tools

PyTorch

TensorFlow

Vector databases

Job description

Hewlett Packard Enterprise Development LP is seeking an AI Data Platform Field Architect to lead data-first AI solution designs for enterprise customers. This is a Remote/Teleworker role with primary work-from-home duties and frequent collaboration with field teams.

You will drive architecture from data requirements and access patterns, scope GPU-based environments, and align solutions with business value across RAG, inference, and training workflows.