What the AI engineering role actually is, how it differs from ML engineering, and the vocabulary the rest of the roadmap assumes.
10 articles
Listed inWhat is an AI Engineer?Introductionon
The role is context plumbing, schema design, evaluation, and cost control — not gradients. What you need to know to start, and what you can safely defer.
Listed inWhat is an AI Engineer?Introductionon
The engineer who builds products on top of existing models rather than training new ones from scratch.
Listed inInferenceIntroductionon
Running a trained model to get output, and the cost, latency, and throughput levers around it.
Listed inCommon TerminologyIntroductionon
Inference, training, embeddings, context, agents, and the rest of the shared vocabulary in one place.
Listed inImpact on Product DevelopmentIntroductionon
How probabilistic components change specs, QA, release cadence, and what "done" means.
Listed inAI Engineer vs ML EngineerIntroductionon
Where the two roles overlap, where they diverge, and which problems belong to each.
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 an LLM is, what it predicts, and why that single mechanism produces such broad behaviour.
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.