Mirantis · Posted 2d ago
Senior Software Engineer (AI Infrastructure)
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇺🇸 United States
- Contract
- Full-time
- Role
- ML engineering
- Source
- Stapply Data
Stack
- LLM
- Go
- TypeScript
- Kubernetes
- CI/CD
The posting
Join our team as a Senior Software Engineer (AI Infrastructure) and play a key role in designing and building LLM serving infrastructure on Kubernetes. You will be responsible for deployment, GPU scheduling, scaling, and model lifecycle management. Additionally, you will package the platform for enterprise environments, integrate the serving layer with the platform's API gateway, and build observability for operating GPU inference in production. You should have deep hands-on Kubernetes experience, strong Go programming skills, and 5+ years of software engineering experience in infrastructure, platform, or distributed systems.
- Design and build LLM serving infrastructure on Kubernetes, including deployment, GPU scheduling, scaling, and model lifecycle management. - Package the platform for enterprise environments, including Helm-based installs, upgrades, and restricted/offline networks. - Integrate the serving layer with the platform's API gateway, identity, and metering services, and build observability for operating GPU inference in production.
- Deep hands-on Kubernetes experience: building and operating production workloads and Helm charts, not just consuming managed clusters - Strong Go programming skills; solid CI/CD and infrastructure-as-code skills - Experience with GPU workloads or LLM inference, or strong adjacent systems experience and a track record of learning fast - 5+ years of software engineering experience in infrastructure, platform, or distributed systems - Comfortable with high autonomy on a small, remote-first, written-culture team - Fluency with AI-assisted development tools (Claude Code, OpenAI Codex) as part of your daily engineering workflow - Inference performance work (quantization, batching, caching) or distributed serving frameworks - Enterprise deployment experience: air-gapped installs, SSO/OIDC, supply-chain security - UI development experience (e.g. React/TypeScript), useful as the product's management surfaces grow - Open-source contributions in the Kubernetes or ML-infrastructure ecosystems