AI Engineer

The only genuinely non-negotiable prerequisite in the entire Atlas.

The specific mathematics that appears in practice, and nothing beyond it.

Systems whose behaviour is learned from data rather than written as rules.

Derivatives, the chain rule, and what it means to walk downhill in a loss landscape.

The optimisation procedure underneath essentially every model in this Atlas.

Many-layered neural networks that learn their own features instead of being handed them.

Layers, weights and activations. The unit of construction for everything downstream.

Letting every position look at every other position. The hinge the modern field turns on.

Attention, feed-forward layers, residuals and normalisation. The block that ate the field.

Transformers trained on enough text to become general-purpose reasoning surfaces.

Vectors, matrices and the operations every model is secretly made of.

Meaning as coordinates. The representation that makes search, clustering and RAG possible.

Give the model the right documents at query time instead of hoping it memorised them.

Specifying a task precisely enough that a probabilistic system does it reliably.

Models that decide what to do next, call tools, and act in a loop rather than answering once.

Version control, review and reproducibility. Assumed silently by every team.

The bridge from a working notebook to a system real users depend on.