Responsibilities
- Design and run Kubernetes environments optimized for AI inference, retrieval, experimentation, and agent execution in secure or isolated settings
- Deploy and operate open-source or open-weight model stacks, model gateways, vector databases, and supporting platform components
- Build reproducible platform automation using Infrastructure as Code and GitOps approaches for stable, auditable delivery
- Manage local registries, package mirrors, secrets, access controls, storage, networking, and observability in environments with limited or no public cloud dependency
- Optimize GPU, compute, and storage usage for reliable AI workloads while maintaining security and data sovereignty requirements
Requirements
- Open-source or open-weight LLM stacks
- Air-gapped or isolated deployment models
- Kubernetes-based platform engineering
- GPU-enabled environments
- Auditable AI operations suitable for sovereignty-sensitive programs
