Context
System Types in System Design Interviews
The old shared ML/AI branch is now split into two separate engineering themes.
The old AI/ML Engineering section tried to keep too many different problems in one route: production ML, MLOps, LLM/RAG, agent systems, historical AI context, and product cases. That was acceptable while the section was small. As it grew, the route stopped being clear because topics with different engineering concerns were still grouped together mostly out of habit.
This page now acts as a bridge: it helps you see where ML Engineering begins, with the model lifecycle, serving, and the feedback loop around the model, and where AI Engineering begins, with LLM products, orchestration, and guardrails.
Two different engineering centers of gravity
ML Engineering
Focus on data, error metrics, model release, and how the system behaves in production after training is done.
- feature pipelines and dataset quality
- model release, calibration, and rollout safety
- serving, latency, and cost control
- retraining, drift, and the next improvement cycle
AI Engineering
Focus on LLM, RAG, and agent systems, plus AI assistants: orchestration, evaluation, product scenarios, and platform decisions around AI products.
- LLM, RAG, and agent architecture
- tool calling, memory, and workflow orchestration
- prompting, safety, and guardrails
- AI assistants, product scenarios, and platform cases
How to choose the right route
Start from models, data, and the working system
If your main question is how to train, release, measure, and operate a model, start with ML Engineering.
Start from the LLM product and its architecture
If your main question is about RAG, agents, AI assistants, guardrails, and product delivery around LLMs, start with AI Engineering.
You will likely need both themes, but in different order
ML Engineering gives you the language of lifecycle and operating discipline, while AI Engineering helps you reason about modern AI products and orchestration on top of models.
New theme
ML Engineering
Models, pipelines, serving, calibration, retraining, feature contracts, and operating ML systems in production.
Open themeNew theme
AI Engineering
LLMs, RAG, agents, AI assistants, guardrails, evaluation, and AI product architecture.
Open themeWhere to go next
- ML Engineering: Designing Models, Pipelines, and the Production Loop - The main introductory chapter for the ML Engineering theme.
- AI Engineering: Designing LLM, Agent, and Copilot Systems - The main introductory chapter for the AI Engineering theme.
- ML Engineering theme - The full list of ML Engineering chapters after the split.
- AI Engineering theme - The full list of AI Engineering chapters after the split.