The systems, abstractions, and operational layers that make AI models usable at scale, from compute and caching to routing, governance, agent hosting, and credential management.

Kubernetes is the dominant container orchestration platform, providing the runtime foundation for modern cloud-native infrastructure, developer platforms, and AI inference workloads.

LLM engineering spans the full stack of building with large language models: training, inference optimization, agent architecture, harness design, and the operational tradeoffs that determine whether model capability translates into reliable software.

LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

The engineering decisions that determine how software behaves under real load, covering durability, observability, testing discipline, performance constraints, and the operational costs of failure.