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

Technical Marketing Engineer (TME) for AI & On-Premises Object Storage    This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: The Technical Marketing Engineer for AI & On-Premises Object Storage serves as the technical champion for HPE’s secure, localized object storage solutions. The role bridges machine learning innovation and high-performance data infrastructure by creating hands-on demonstrations, proof-of-concepts, reference architectures, benchmark workflows, and technical content. The successful candidate will work with enterprise architects, data scientists, infrastructure engineers, Solutions Architects, Sales Engineers, channel partners, Product Management, and Storage Engineering. This position requires strong expertise in object storage, AI/ML pipelines, distributed systems, infrastructure design, performance optimization, and technical communication. Key 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