AI Engineer

You don't need to be a mathematician, but intuition for algebra and stats lets you understand models, not memorise them.

AI runs on data — you must clean and shape it before any model.

Understanding how machines learn from data builds your intuition even when using ready-made models.

Every modern LLM is built on neural networks — understanding the building blocks matters.

Most AI apps today are built on top of hosted models via APIs — this is where practical value starts.

The same model gives wildly different results based on your prompt — it's the cheapest control you have.

Embeddings turn text into numbers machines understand — the basis of semantic search and all retrieval.

The most important pattern in practical AI apps: make the model answer from your data, not just its training.

Frameworks save you from wiring pieces by hand and speed up building complex AI apps.

When prompting and RAG aren't enough, fine-tuning tailors a model to your task or style.

Without measurement you can't tell if your app improved or regressed — evaluation is your compass.

A model on your laptop is worthless until it serves real users reliably and efficiently.

Building safe, fair AI isn't a luxury — it's the responsibility of every professional engineer.