What the AI engineering role actually is, how it differs from ML engineering, and the vocabulary the rest of the roadmap assumes.
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10 articles
Listed inWhat is an AI Engineer?Introductionon
The day-to-day work is context plumbing, schema design, evaluation and cost control rather than model training. The prerequisite that carries most of the weight is ordinary software engineering.
Listed inWhat is an AI Engineer?Introductionon
A single support-reply feature, taken through the failures it hits in production: supplying a refund policy, grounding replies in help-center articles, structuring output for a ticket tool, and handling prompt injection in customer email. The role is the engineering around the model call.
Listed inInferenceIntroductionon
What happens between your request and the answer: time to first token against total response time, why output length drives latency, where token costs accumulate, and the inference failures to handle in code.
Listed inCommon TerminologyIntroductionon
The working vocabulary in one place, ordered so each term builds on the last, plus four confusions that cause real mistakes.
Listed inImpact on Product DevelopmentIntroductionon
Because the same input can produce different output, testing, cost, latency and failure handling all change shape. A feature also inherits its model's published retirement date, which makes migration scheduled work rather than a surprise.
Listed inAI Engineer vs ML EngineerIntroductionon
One role starts with data and produces a model; the other starts with a model and produces a system. Both use the word evaluation for different things, which is where the two talk past each other.
Listed inTrainingIntroductionon
Pre-training, post-training, and RLHF — the parts you rarely do but always need to reason about.
Listed inLarge Language Models (LLMs)Introductionon
What goes into a request, what the model holds while answering, and what comes back. The context window is a shared budget for input and output, and some model limits can be fixed while others have to be designed around.
Listed inAI vs AGIIntroductionon
Separating the systems you can ship today from the general intelligence you cannot.
Listed inRoles and ResponsibilitiesIntroductionon
The day-to-day surface area: prompts, retrieval, evals, latency budgets, and cost control.