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System Design and AI Engineering, by Roni Das. 980 interactive lessons.
Start LearningYes, and a backend developer starts closer than most people think. AI engineering means building products on top of trained models: calling them through an API, giving them the right data (RAG), connecting tools (agents), and proving the results are good enough (evals). You keep APIs, databases, caching, queues, retries and tests. What is new is a component whose answer changes from run to run, so you learn prompts as code, retrieval, and measuring quality over many cases. You do not need to train models for most AI engineer roles.
Moving from backend to AI? The AI Engineering course is written for working engineers: how models behave, RAG, agents, evals and production, each with real measurements you can repeat.
An AI engineer builds features where a model does part of the work: answering questions from company documents, sorting support tickets, writing a first draft, or an agent that takes actions. Around the model sits ordinary backend work: an API, a database, a queue, a cache, logins and monitoring.
The difference is the model itself. A normal function gives the same output for the same input. A model may not. So you cannot check one case and move on; you build a set of test cases and measure how often it is right. That habit, evals, is the biggest change for a backend developer, and the most valued skill in interviews.
Teams hiring for this work say they want both kinds of engineer. Perplexity describes its agents team as "backend, full-stack, and AI/ML engineers" working together.
“The Agents team consists of backend, full-stack, and AI/ML engineers who collaborate to build harnesses and AI systems powering delightful agentic experiences.”
“We're looking for applied AI engineers to help bring Codex agents from impressive demos to dependable tools.”
“We recommend finding the simplest solution possible, and only increasing complexity when needed.”
Quotes copied from the pages linked, read on 23 October 2026.
Calling a model is an API call with a timeout, a retry and a rate limit. Asking for JSON with a schema, and checking it in code, is the same contract thinking you already use.
RAG is search: split documents into chunks, store them with an embedding (a list of numbers that stands for meaning), and search by meaning, often together with ordinary keyword search.
Tokens cost money and time. Caching repeated prompts, sending easy questions to a cheaper model, and keeping prompts short are cost work you already know how to think about.
An agent run can take minutes and call many tools. It needs the same things as any long job: saved progress, safe retries and a way to resume after a crash.
Build a fixed set of real cases, read the failures first, then measure. This replaces "it worked when I tried it" with a number you can track.
Prompts are code: versioned, tested and reviewed. And any text the model reads, such as an email or a web page, may contain instructions, which opens a new class of security bug called prompt injection.
For a working backend developer: about 8 to 10 hours a week, a rough guide and not a promise. Each month ends with something you can show. Free lessons are marked; the rest are in the AI Engineering course.
Days 1 to 30
Before building, learn what you are calling: what a token is, why answers vary, what the context window limits, and how to get reliable JSON back.
Prove it: Wrap a model call in your own API with a schema check, a timeout, a retry and a log line for cost.
Days 31 to 60
Most AI engineering work in companies is RAG. It is also where backend skills (indexes, pipelines, caching) help most.
Prove it: Build question answering over 100 real documents, and measure how often search finds the right chunk.
Days 61 to 90
This month turns a demo into something a team would ship, and gives you the stories interviewers ask for.
Prove it: Write 50 eval cases for your RAG project, change one thing, and show the score and the cost before and after.
There is no single standard loop, and the companies we checked do not publish theirs. These are the round types to prepare for; each company mixes them differently. Your backend experience is an advantage in the first two.
Coding
Usually ordinary coding, often in Python. Some companies ask you to call a model API and parse its output safely.
AI system design
Design a RAG service, a support agent or an LLM gateway. This is a system design round with new parts, and the worked answer below shows the shape.
Evals and debugging
"Answers got worse after a change. What do you do?" They want to hear: look at failing cases, find the step that broke (search or generation), measure before and after.
Projects and behaviour
One or two things you built, explained with numbers: how good it was, what it cost, what failed. A backend project where you added a model counts.
Here is the shape of a good answer to the most common AI design prompt, from a backend developer's point of view.
Design a service that answers employee questions from 50,000 internal documents.
One worked answer is not enough for the loop.
Each question has why it is asked, a full answer, what the interviewer listens for, the follow-ups, the common mistakes and a diagram (180 diagrams in all). A PDF you keep, junior to staff level.
India: UPI, cards or netbanking through Razorpay. Elsewhere: card or PayPal. Download at once from My packs.
The AI Engineering course has 210 lessons, and 10 of them are free to read first, with no card needed. It follows the order of this plan: how models behave, prompts, RAG, agents, evals, security and production, each built around a real experiment with real numbers. One payment, lifetime access.
Free 15-min Interview Readiness Check
A short call. I tell you the 2 or 3 things to fix first for your target role. It costs nothing, and it is booked on Topmate.
Also useful: AI engineer roadmap, AI engineer interview questions, Agentic AI engineer guide, Inference engineer guide and SDE-2 system design interview.
