Technical Product Manager, AI Storage

Mirantis is the Kubernetes-native AI infrastructure company, enabling organizations to build and operate scalable, secure, and sovereign infrastructure for modern AI, machine learning, and data-intensive applications. They are seeking a Technical Product Manager to own the storage strategy for k0rdent AI, focusing on defining how operators provision, tier, and scale storage across GPU clusters for large-scale training and inference.

Responsibilities:

• Own the vision, roadmap, and priorities for k0rdent AI storage, defining how customers design, automate, and operate every aspect of their storage stack, including parallel file systems, object storage, tiering, snapshots, data protection, and more
• Translate requirements from NeoClouds, GPU clouds, telcos, sovereign clouds, and enterprise platform teams into clear product direction
• Partner with engineering and architecture to define requirements, evaluate trade-offs, and ship secure, scalable, reliable storage capabilities
• Manage the storage backlog, using feedback from production deployments and design partners to refine roadmap priorities and positioning
• Define positioning, packaging, and competitive differentiation for k0rdent AI storage
• Create field-facing assets, including technical briefs, battlecards, and reference architectures; support strategic accounts as the storage product lead
• Represent Mirantis at events, analyst briefings, and customer advisory boards; engage storage, silicon, and ecosystem partners on reference architecture alignment

Requirements:

• 5+ years in product management, technical product management, or a senior technical role owning a storage product, platform, or large-scale storage estate
• Hands-on familiarity with parallel and distributed file systems (Lustre, GPFS/Spectrum Scale, BeeGFS, WEKA, VAST, DAOS), S3-compatible object storage, and block storage at scale
• Fluency in Linux storage, Kubernetes storage (CSI, dynamic provisioning, storage classes), software-defined storage, hyperscale or service provider storage, or storage automation
• Hands-on experience with modern GPU cluster storage, including GPUDirect Storage and RDMA data paths, NVMe-oF, hot/warm/cold tiering for training datasets and checkpoints, and high-throughput ingest pipelines