Senior AI Infra Engineer - Kubernetes & LLMs

Mirantis; United States; Remote; USD 190,000 - 230,000; Full time

Benefits offered by this job

Competitive compensation package

Professional development

Conferences and tech talks

Remote-first culture

Hackathons

Job summary

Mirantis is building an enterprise AI infrastructure product to run and govern large language models on customer Kubernetes clusters. Join a small senior team with broad ownership of the model-serving layer and its path to production.

You will design and implement LLM serving infrastructure, including deployment, GPU scheduling, scaling, and lifecycle management, while packaging for restricted/offline networks and ensuring enterprise readiness.

Qualifications

• 5+ years of software engineering experience in infra or distributed systems.
• Deep hands-on Kubernetes experience building/operating production workloads and Helm charts.
• Experience with GPU workloads or LLM inference or strong adjacent systems experience.
• Strong Go programming skills; solid CI/CD and infrastructure-as-code skills.
• Fluency with AI-assisted development tools (Claude Code, OpenAI Codex) as part of daily workflow.

Responsibilities

• Design and build LLM serving infrastructure on Kubernetes: deployment, GPU scheduling, scaling, and model lifecycle management.
• Package the platform for enterprise environments: Helm-based installs, upgrades, and restricted/offline networks.
• Integrate the serving layer with the platform's API gateway, identity, and metering services.
• Build observability for operating GPU inference in production (serving metrics, GPU telemetry).
• Contribute across a multi-service codebase and help set engineering direction through design docs and reviews.

Skills

Go programming

Kubernetes

Distributed systems

CI/CD pipelines

LLM inference

GPU workloads

Tools

Kubernetes

Helm

CI/CD pipelines

Job description

Mirantis is building an enterprise AI infrastructure product to run and govern large language models on customer Kubernetes clusters. Join a small senior team with broad ownership of the model-serving layer and its path to production.

You will design and implement LLM serving infrastructure, including deployment, GPU scheduling, scaling, and lifecycle management, while packaging for restricted/offline networks and ensuring enterprise readiness.