AI engineer
A live, voice-based session shaped for the AI engineer role. An interviewer works through the systems you would actually build with, from retrieval to agents to evaluation, and presses on the tradeoffs. Minutes after you finish, you get a report that shows which points you covered and which ones you missed.
Know the difference
The two titles blur together in job posts, and the loops pull in different directions.
You build products on top of foundation models. The loop probes how you wire a large language model into a system that stays reliable.
RAG pipelines, embeddings, and keeping answers tied to real sources.
Planning, tool calls, and knowing when an agent is worth its cost.
Measuring output quality, defending against bad inputs, and holding a latency budget.
You build the models themselves. The loop probes how you frame a problem, train, and ship something that holds up on real data.
Turning a vague goal into a modeling problem, with the right features and labels.
Classic modeling, offline and online evaluation, and reading the tradeoffs in the numbers.
Feature stores, training pipelines, serving, and monitoring a model in production.
Topic areas
These are the areas an AI engineer is expected to reason about live.
Tokens, context windows, structured output, and function calling. Expect to show when shaping the prompt or the context fixes a failure, and when it only masks one.
Chunking, embeddings, vector search, and reranking. The hard part is measuring retrieval quality and explaining why the model grounded on the wrong passage.
Planning, tool calls, and multi-step orchestration. Explain where an agent earns its cost and where a plain pipeline is the safer choice.
Building an eval set, judging output quality, and catching regressions before they ship. You will be pushed to measure hallucination, faithfulness and accuracy separately.
Prompt injection, grounding and citations, handling sensitive data, and validating output. The question is how you keep a model honest when the input turns adversarial.
Model selection, caching, streaming, and token budgets, plus the tracing and monitoring that keep a live system healthy. This is the LLMOps side of the role.
The loop
Most loops open with a recruiter and behavioral screen, then a hands-on build where you wire up a small feature, a design round for something like a retrieval assistant or a support agent, and a final round where interviewers press on your evaluation strategy and cost tradeoffs. What moves between levels is how much judgment you are expected to bring.
You are checked on fundamentals: calling a model, a basic retrieval flow, and why a model hallucinates. They want to see that you can build with these tools and reason about what they return.
You are expected to build a feature end to end and defend it: retrieval quality, a simple eval, and an honest read on cost and latency. Debugging a flaky pipeline on the spot is common.
The bar moves to architecture and judgment: evaluation and guardrails as first-class concerns, when not to use a model at all, and how you would lead an ambiguous problem to a decision.
The report
Each score points to your own words and ends with one practice step for the next session.
Every response is shown in your own words, so you can see where the reasoning thinned out.
It names what you did well, the fix that matters most, and a specific thing to practice next.
Each answer is checked for the retrieval, evaluation and cost decisions expected in the role.
Post-interview report
Points covered
Expect system design for something like a retrieval assistant or an agent, detailed questions about evaluation, guardrails, cost and latency, and behavioral questions about systems you have shipped. Follow-up questions get harder when an answer leaves a claim unsupported.
Yes. AI engineering centers on building with foundation models: retrieval, agents, evaluation, guardrails, and the cost and latency of running them in production. Machine learning engineering centers on framing, training, and shipping the models themselves. This practice is tuned to the first.
Yes. Paste the job posting, or a link to it, and the session shapes its questions around that exact role and level. You can also add your resume so the conversation draws on your own projects.
No. This is a conversation about design, tradeoffs, and your past work, which is where AI engineer loops often separate strong candidates from the rest. Practice it alongside the hands-on build round you do on your own.
A question list stays silent. Here you have to answer by voice, defend your choices under follow-up questions and review the exact moments where your reasoning held up or slipped.
Paid monthly plans include 150 or 400 interview minutes.
Available now
Practice against your target role, handle adaptive follow-ups, and get a detailed report tied to your exact answers.
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