I’ve been working as an AI Engineer for ~2 years. If you’re trying to break into AI Engineering, these are the topics I’d prioritize learning: • RAG — Retrieval-Augmented Generation • Embeddings & Vector Databases • Quantization — AWQ, GPTQ, GGUF, FP16, INT8, INT4 • RAG Evaluation — RAGAS • Model Routing & Fallbacks • Observability — logs, metrics & traces • MCP — Model Context Protocol • AI Agents & Agentic Patterns • Memory & Context Management • Guardrails & deterministic input/output validation • Inference Engines — vLLM, TensorRT, SGLang • Load Balancing & Queuing • Local vs Cloud Inference • Backend Fundamentals & System Design The biggest mistake I see is focusing only on prompting and calling LLM APIs. AI Engineering is much more about building reliable, scalable systems around models.