Senior AI Infra Engineer: Kubernetes, GPU & LLM Serving

Mirantis, Inc.; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Professional development and training

Conferences and technical talks

Company outings and hackathons

Competitive compensation and benefits

Job summary

Mirantis, Inc. is seeking an experienced engineer to design and operate LLM serving infrastructure on Kubernetes, including GPU scheduling, scaling, and model lifecycle management.

The role involves packaging for enterprise environments with Helm and offline networks, and integrating the serving layer with API gateway, identity, and metering services. You will build observability for GPU inference in production and contribute to a multi-service codebase, shaping engineering direction through

Qualifications

• 5+ years of software engineering experience in infrastructure or distributed systems.
• Deep hands-on Kubernetes experience: running production workloads and Helm charts.
• Experience with GPU workloads or LLM inference, or related learning ability.
• Strong Go programming skills; solid CI/CD and infrastructure-as-code skills.
• Fluency with AI-assisted development tools (Claude Code, OpenAI Codex).
• Comfortable with high autonomy on a small, remote-first team.

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

Kubernetes

Go

CI/CD

Infrastructure as Code

Remote-first team

Tools

Helm

Terraform

OpenAI Codex

Claude Code

Job description

Mirantis, Inc. is seeking an experienced engineer to design and operate LLM serving infrastructure on Kubernetes, including GPU scheduling, scaling, and model lifecycle management.

The role involves packaging for enterprise environments with Helm and offline networks, and integrating the serving layer with API gateway, identity, and metering services. You will build observability for GPU inference in production and contribute to a multi-service codebase, shaping engineering direction through