Senior Software Engineer, AI Infrastructure

Mirantis

. Design and build LLM serving infrastructure on Kubernetes, including deployment, GPU scheduling, scaling, and model lifecycle management .

Posted 9/15/2026full-timeRemote • KazakhstanSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building LLM serving infrastructure on Kubernetes, with strong capabilities in GPU scheduling, model lifecycle management, and enterprise deployment. Proficient in Go programming and CI/CD practices, with a focus on observability and integration within multi-service environments.

Highest-signal resume keywords

Kubernetes Infrastructure ManagementGo Programming SkillsCI/CD and Infrastructure-as-CodeGPU Workloads and LLM InferenceHelm Chart Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

KubernetesGo ProgrammingCI/CDInfrastructure-as-CodeHelmGPU WorkloadsLLM InferenceObservabilityModel Lifecycle ManagementAPI Integration

Soft Skills

High AutonomyCollaborationWritten Communication

Tools & Technologies

Claude CodeOpenAI Codex

Industry Keywords

Distributed SystemsEnterprise DeploymentAir-Gapped InstallsSSO/OIDCSupply-Chain SecurityUI DevelopmentReactTypeScriptOpen-Source ContributionsML-Infrastructure

Tech Stack

Tools & technologies

Distributed SystemsKubernetesReactTypeScriptGo

About the role

Key responsibilities & impact

Design and build LLM serving infrastructure on Kubernetes, including deployment, GPU scheduling, scaling, and model lifecycle management
Package the platform for enterprise environments with 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, including serving metrics and GPU telemetry
Contribute across a multi-service codebase
Help set engineering direction through design documents and code reviews
Join a small senior team early with broad ownership of the model-serving layer and its path to production

Requirements

What you’ll need

5+ years of software engineering experience in infrastructure, platform, or distributed systems
Deep hands-on Kubernetes experience building and operating production workloads and Helm charts
Experience with GPU workloads or LLM inference, or strong adjacent systems experience and a track record of learning fast
Strong Go programming skills
Solid CI/CD and infrastructure-as-code skills
Fluency with AI-assisted development tools, including Claude Code and OpenAI Codex, as part of daily engineering workflow
Comfortable with high autonomy on a small, remote-first, written-culture team
Nice to have: inference performance work, including quantization, batching, or caching
Nice to have: distributed serving frameworks
Nice to have: enterprise deployment experience, including air-gapped installs, SSO/OIDC, and supply-chain security
Nice to have: UI development experience, such as React/TypeScript
Nice to have: open-source contributions in Kubernetes or ML-infrastructure ecosystems

Benefits

Comp & perks

Professional development and training
Attend conferences and working groups
Company outings, happy hours, hackathons, and tech talks
Competitive compensation package with a strong benefits plan
Work with passionate colleagues and Fortune 500 and Global 2000 customers
Cutting-edge, open-source innovation
High-energy environment valuing openness, collaboration, risk-taking, and continuous growth