Technical Marketing Engineer – AI, On-Premises Object Storage

Hewlett Packard Enterprise

. Serve as the technical champion for HPE’s secure, localized object storage solutions .

Posted 10/27/2026full-timeUnited StatesSeniorLead💰 $157,000 - $361,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and optimizing secure object storage solutions, integrating AI/ML workflows, and delivering technical enablement. Proficient in technical writing and mentoring, with a strong foundation in distributed storage systems and performance analysis.

Highest-signal resume keywords

S3 API-Based Object StorageAI/ML Training WorkflowsDistributed Storage Systems DesignTechnical Writing and Public SpeakingPerformance Benchmarking and Load Testing

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonGoAI/ML WorkflowsData Lakehouse ArchitecturesBenchmarking with fioErasure CodingReplicationMetadata ManagementPerformance OptimizationInfrastructure Automation

Soft Skills

MentoringTechnical CommunicationCustomer Enablement

Tools & Technologies

DockerKubernetesApache IcebergDelta LakeParquetPyTorchTensorFlowRayHugging Face TransformersContainer Storage Interface (CSI)

Industry Keywords

Technical MarketingStorage EngineeringInfrastructure ArchitectureEnterprise Hardware SizingData PreparationCheckpointingCachingStorage ThroughputInfiniBand100GbE to 400GbE Ethernet

Tech Stack

Tools & technologies

ApacheDockerFlashKubernetesNode.jsPythonPyTorchRayTensorflowGo

About the role

Key responsibilities & impact

Serve as the technical champion for HPE’s secure, localized object storage solutions
Design, build, and maintain demonstration environments, infrastructure blueprints, hardware sizing guides, and validated reference architectures
Combine on-premises object storage with AI compute infrastructure
Develop AI/ML benchmark workflows, proof-of-concepts, and Jupyter notebooks
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 technical content
Deliver technical enablement for Solutions Architects, Sales Engineers, channel partners, and customers
Evaluate storage I/O behavior, bottlenecks, scaling limitations, and workload performance
Provide field-driven technical feedback to Product Management and Storage Engineering
Mentor less-experienced engineers and contribute to software systems design, infrastructure automation, and technical innovation

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related discipline, or equivalent practical experience
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 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 PyTorch, TensorFlow, Ray, or Hugging Face Transformers
Proficiency in Python or Go
Demonstrated success in technical writing, public speaking, technical video creation, customer enablement, or translating complex infrastructure topics
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
Familiarity with InfiniBand, RoCE/RDMA, and 100GbE to 400GbE or higher Ethernet environments
Experience with Docker, Kubernetes, and Container Storage Interface (CSI) integrations
Expertise in benchmarking and load testing with fio, warp, mdtest, or custom workload generators
Ability to analyze performance data and communicate storage behavior, scalability, and optimization opportunities clearly
Ability to translate complex infrastructure and AI/ML concepts into technical narratives, validated solutions, and customer guidance

Benefits

Comp & perks

Comprehensive health and wellbeing benefits supporting physical, financial, and emotional wellbeing
Personal and professional development programs
Career growth opportunities
Flexible work arrangements to manage work and personal needs
Hybrid work arrangement