AI Engineer Competency Map

The AI ​​Engineer Competency Map: The first category is Model Understanding Capability. The core competency is understanding the behavior of LLMs, such as Transformers, autoregressive generation, and the Prompt mechanism. This layer determines whether you truly understand large models, rather than treating them as black-box APIs. The second category is AI Application Capability, mainly RAGs and Agents, which are the two core technical models for current AI applications. RAGs solve the knowledge access problem, and Agents solve the automation problem of complex tasks. The third category is Engineering Implementation Capability. This category is essentially about building AI capabilities into systems. It includes API services, RAG services, Agent services, model adaptation layers, etc. The fourth category is AI Platform Capability. When enterprises begin to use AI on a large scale, they will need AI platforms, such as AI Gateways, Prompt management, model routing, monitoring, and cost control. This layer represents the core competency of an AI architect.