About the role
About Mirantis 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. By combining open source innovation with deep expertise in Kubernetes orchestration, Mirantis empowers platform engineering teams to deliver composable, production-ready developer platforms across any environment—on-premises, in the cloud, at the edge, or in sovereign data centers. As enterprises navigate the growing complexity of AI-driven workloads, Mirantis delivers the automation, GPU orchestration, and policy-driven control needed to manage infrastructure with confidence and agility. Committed to open standards and freedom from lock-in, Mirantis ensures that customers retain full control of their infrastructure strategy. Job Summary Mirantis is looking for a commercially driven, deeply technical Product Manager to own AI inference and model serving for k0rdent AI, our control plane for GPU infrastructure and distributed AI workloads. This role sits at the intersection of AI inference, cloud-native infrastructure, distributed systems, and performance engineering. You will define how NeoClouds and Enterprise customers deploy, scale, and operate production inference services while extracting maximum performance from the underlying GPU, network, and storage infrastructure. This role owns product strategy and solution development for inference products across on-premises, cloud, and edge environments. The scope includes serverless inference, dedicated endpoints, workload placement, autoscaling, routing, lifecycle management, observability, and full-stack performance optimization. This person will define how customers run production model-serving workloads at scale while improving latency, throughput, utilization, reliability, cost, and operational control. The ideal candidate has experience with high-performance infrastructure products and understands how production systems behave under real-world load. They should be comfortable reasoning across the full stack, identifying performance bottlenecks, evaluating system design trade-offs, and translating technical insight into clear product
