Technical Marketing Engineer (TME) for AI & On-Premises Object Storage

Responsibilities

• Design, build, and maintain realistic demonstration environments, infrastructure blueprints, hardware sizing guides, and validated reference architectures combining on-premises object storage with AI compute infrastructure.
• Develop AI/ML benchmark workflows, proof-of-concepts, and Jupyter notebooks that demonstrate performance optimization for data preparation, ingestion, checkpointing, and large-scale unstructured data processing.
• Create technical whitepapers, deployment guides, solution briefs, competitive analyses, technical videos, and other content explaining storage performance, scalability, and AI infrastructure design.
• Deliver technical enablement for internal Solutions Architects, Sales Engineers, channel partners, and customers to address complex on-premises infrastructure requirements and objections.
• Evaluate storage I/O behavior, bottlenecks, scaling limitations, and workload performance using industry-standard benchmarking tools and custom load generators.
• Provide field-driven technical feedback to Product Management and Storage Engineering regarding customer requirements, product capabilities, scale limitations, and performance opportunities.
• Mentor less-experienced engineers and contribute to software systems design, infrastructure automation, and technical innovation.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related discipline, or equivalent practical experience.
• Typically ,10 + years of experience in technical marketing, systems engineering, storage engineering, infrastructure architecture, or a related technical field.
• Deep experience with S3 API-based object storage, including bucket policies, lifecycle management, object locking, and multi-tenant data isolation.
• Strong understanding of data lakehouse architectures and data formats such as Apache Iceberg, Delta Lake, and Parquet.
• Practical experience designing or operating distributed storage systems, including erasure coding, replication, metadata management, and cluster scaling.
• Experience with AI/ML training workflows and frameworks such as PyTorch, TensorFlow, Ray, or Hugging Face Transformers.
• Proficiency in Python or Go for infrastructure automation, API integrations, developer tooling, or systems development.
• Demonstrated success in technical writing, public speaking, technical video creation, customer enablement, or translating complex infrastructure topics for diverse audiences.
• Strong understanding of AI data ingestion, data preparation, checkpointing, caching, and storage throughput requirements.
• Knowledge of enterprise hardware sizing, including node density, CPU and RAM configurations, NVMe flash tiers, GPU clusters, and storage fabrics.