Asks for

The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”
The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”
The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”
The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving”
The ad says“Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability”

Company Description

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 Description

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 requirements, architecture direction, and customer-facing solutions.

Responsibilities

Own product strategy, roadmap, and lifecycle for inference and model serving, including serverless inference, dedicated endpoints, autoscaling, routing, KV cache management, and the related observability
Lead deep technical discovery with NeoClouds, sovereign clouds, and enterprise platform teams, and translate findings into prioritized requirements and architecture direction
Partner with engineering on system design trade-offs across runtime integration, GPU scheduling, network, storage, and serving topology, including disaggregated serving and multi-model serving
Define positioning grounded in measurable outcomes: latency distributions, throughput per GPU, utilization, tail reliability, and cost per tokens
Drive go-to-market execution: pricing and packaging, reference architectures, sizing guides, PoC playbooks, and direct engagement with customers, analysts, and ecosystem partners

Qualifications

7+ years in product management, technical product management, or a senior technical role owning AI/ML and inference product(s)
Strong understanding of production AI inference, including model serving, serverless execution, dedicated endpoints, autoscaling, routing, workload placement, observability, and reliability
Proven capability to reason about performance trade-offs across GPU, network, storage, orchestration, and runtime layers, and to translate low-level technical capability into business value such as TTFT, throughput per GPU, and TCO
Working knowledge of modern inference runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo, Triton) and the optimization patterns that matter in production: continuous batching, KV cache management, cold starts, prefill versus decode, disaggregated serving, and multi-model serving
Credibility with engineering leaders and infrastructure operators, including comfort in production architecture reviews and technical commercial conversations with platform engineering buyers

Why you’ll love Mirantis

Build the token factory foundation for the AI cloud era, working directly with leading GPU cloud operators, NeoClouds, sovereign clouds, and AI-first enterprises
Collaborate with a world-class, distributed team committed to openness and technical excellence
Shape the product narrative and influence go-to-market success

Additional Information

What does Mirantis offer you?

Work with an established Silicon Valley leader in the cloud infrastructure industry.
Work with exceptionally passionate, talented and engaging colleagues, helping Fortune 500 and Global 2000 customers implement next-generation cloud technologies.
Be a part of cutting-edge, open-source innovation.
Thrive in the high-energy environment of a young company where openness, collaboration, risk-taking, and continuous growth are valued.
Professional development and training.
Attend conferences and working groups.
Customized workstation (macOS, Windows).
A competitive compensation package with strong benefits plan and stock options.

It is understood that Mirantis, Inc. may use automated decision-making technology (ADMT) for specific employment-related decisions. Opting out of ADMT use is requested for decisions about evaluation and review connected with the specific employment decision for the position applied for. You also have the right to appeal any decisions made by ADMT by sending your request to isamoylova@mirantis.com

By submitting your resume, you consent to the processing and storage of your personal data in accordance with applicable data protection laws, for the purposes of considering your application for current and future job opportunities.

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