AI Engineer

The software engineering bedrock every AI Engineer needs before touching models.

The minimum viable math to reason about models, not just call them.

AI is data-bound; you must wrangle, store, and query it fluently.

The fundamentals that frame every modeling decision, interview, and baseline.

Neural networks from first principles — the substrate of modern AI.

How the models you'll build on actually work under the hood.

Extracting reliable behavior from LLMs through structured prompting.

The practical layer of calling, controlling, and optimizing model providers.

Grounding LLMs in your own data — the most common production AI pattern.

Building systems where LLMs plan, use tools, and act autonomously.

Customizing models when prompting and RAG aren't enough.

You can't ship what you can't measure — rigorous eval is the senior skill.

Architecting real products around inherently probabilistic models.

Taking models from notebook to reliable, observable production.

The platform skills to deploy and scale AI systems in production.

Shipping AI responsibly — the differentiator for production-grade engineers.

Legal, ethical, and compliance literacy expected of senior AI engineers.

Converting skills into a job offer at a top company.