Read a real-world backend job description for an AI product with us, then follow five candidates through the same deep dive and design discussion.
We do not give you a list of questions to learn by heart. Real interviews do not work like that. An interviewer listens to you first, and then asks about what you said, deeper and deeper, until they find where your knowledge stops. You cannot learn that from a list. You can learn it from watching conversations, and then by practising your own.
So we start where a real application starts: with a job description. We read it together, line by line, and say honestly what it asks for, what it does not say, and who should apply. Then you see how the interviews for this role usually run. Then you follow five different people through the same interview, turn by turn, with notes on what the interviewer is checking, what works, and what goes wrong. At the end, you prepare for your own interview.
The company and the candidates are not real. The job description is a composite: we built it from the patterns that repeat in real Backend Engineer for AI products postings. The conversations are examples, written from our experience of real interviews on both sides of the table.
The Backend Engineer posting that this guide uses, built from patterns in real postings at AI product companies, and a first read to find what the job really is.
What the Casewell backend posting really requires, what is only a wish, what it leaves out, who should apply, and the questions to ask at each stage.
The five interviews in Casewell's process, how to work in a coding round where an AI assistant is allowed, and how a serious interviewer goes from what you say to the edge of what you know.
The same technical interview with five different people. Each conversation starts from what the candidate has built, and goes deeper from there.
Samuel has nine years of payment systems at a bank and designs the upload pipeline almost perfectly, and the interview finds his edge where the model's output changes from one run to the next.
Emily ships AI chat products fast and knows streaming better than most backend engineers, and the interview finds her edge in tenant isolation and concurrency, where she trusted the framework.
Minh finished his degree in May and has one internship, and the interview finds strong fundamentals, a clear way to reason about the cache bug, and an honest edge at scale.
Arash has eight years on one huge storage service, and the interview finds deep distributed-systems skill, a correct disagreement about row-level security, and an edge in product and the legal domain.
Claire comes from health-records software, where a privacy mistake is a legal event. Her interview shows isolation and threat modelling at their best, open use of AI in the coding round, and her edge in fair queueing and cost.
A practice plan for backend roles in AI products - drill-down notes for a system you own, the AI-allowed coding round, the multi-tenant design problem, honest answers at the edge, and a three-week plan.
