AI Data Platform Field Architect

This is a fully remote position, open to applicants in Florida, +4 more states.

📋 Description

• Facilitate discussions on data-centric AI architecture, focusing on data attributes, lifecycle, volume, velocity, distribution, locality, and movement.

• Develop AI solutions that enhance data access patterns, manage AI pipeline data flow, and improve efficiency in metadata, indexing, and retrieval.

• Suggest performance optimizations that improve cost efficiency, reliability, and trustworthiness.

• Assess the effects of data design on model performance, latency, GPU utilization, ingestion requirements, and overall cost.

• Lead technical discovery sessions with enterprise clients to identify, shape, and qualify opportunities within the AI Factory.

• Convert business goals into scalable AI architectures and solution designs.

• Provide guidance to CTOs, Heads of AI, and Data Engineering leaders.

• Propel deal progression by aligning technical solutions with measurable business results.

• Determine the scope and size of AI Factory environments based on GPU configurations, data volumes, throughput, model types, and workloads.

• Establish performance benchmarks for ingestion, preparation, storage, retrieval, and GPU utilization.

• Assist in optimizing time-to-first-token, throughput, and cost efficiency.

• Explain the roles of modern data platforms within AI workflows, including S3 object storage, data pipelines, vector databases, and AI frameworks.

• Position data platforms as key enablers of AI performance and tailor solutions to meet customer requirements.

• Collaborate with Product Management to influence roadmap priorities and relay field feedback.

• Create and present technical materials such as reference architectures, design patterns, whitepapers, conference presentations, and publications.

⛳️ Requirements

• Over 8 years of experience in technical presales, solutions architecture, or field CTO positions.

• Deep understanding of AI/ML workflows, including Retrieval-Augmented Generation (RAG) and model inference/deployment.

• Capability to lead architecture design based on data requirements and access patterns instead of infrastructure-first methods.

• Skill in mapping and optimizing end-to-end data flow throughout the AI lifecycle.

• Experience in defining, developing, and enhancing AI Factory offerings in collaboration with Product Management and solution teams.

• Proven track record of contributing to reference architectures and influencing product direction and roadmap priorities.

• Background in sizing and designing GPU-based environments tailored for AI workloads.

• Experience collaborating with AI/ML or data engineering teams.

• Knowledge of data pipelines, data lakes, object storage, and performance metrics for large-scale data access.

• Ability to align architectures with workload requirements while assessing performance, cost, and complexity trade-offs.

• Demonstrated ability to lead customer discovery sessions and translate requirements into effective technical solutions.

• Excellent communication skills for engaging both technical and executive audiences.

• Preferred/nice-to-have: Exposure to HPC or distributed computing; familiarity with PyTorch, TensorFlow, vector databases; experience with cloud and hybrid AI infrastructure; knowledge of storage technologies; collaboration with Product Management.

🏝️ Benefits

• Comprehensive health and wellness benefits that support physical, financial, and emotional wellbeing.

• Opportunities for personal and professional development.

• Flexible management of work and personal needs.

• An inclusive workplace culture.

• Employee benefits information provided through HPE Rewards.