AI Engineering
In simple words
The bridge from a working notebook to a system real users depend on.
The fuller explanation
AI engineering is the discipline of making probabilistic components behave acceptably inside deterministic products. It is where most of the industry's actual open roles are, and it is a distinctly different job from training models.
The problems are unfamiliar to ordinary backend engineering in specific ways. Latency is dominated by a component you do not control. Cost scales with tokens rather than requests, so a small prompt change can multiply your bill. Correctness is a distribution rather than a boolean. Failures are silent - the system returns a fluent, well-formed, wrong answer with a 200 status code.
The response is a small set of disciplines applied relentlessly: evaluation harnesses that run in CI, observability that captures full traces rather than metrics, explicit fallbacks for every model call, guardrails at the boundaries, and a cost model you actually watch. None of it is glamorous. All of it is the difference between a demo and a product.
Learn these first
Real prerequisites, taken from the map rather than guessed.
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