FREE · APPLIED AI + ML ENGINEERING INTERVIEWS
Free interview prep for Applied AI & ML engineers.
Good engineers aren’t prompted. They’re practiced.
Explained multiple-choice questions, graded Python, NumPy and PyTorch labs, system design cases and mock interview practice, in one focused workspace.
PREPARE FOR EVERY ROUND
Everything an AI or ML interview loop asks of you.
Concept questions
250 original multiple-choice questions on retrieval, agents, evaluation, modelling and engineering foundations, each with a worked explanation.
Coding labs
40 labs in Python, NumPy, pandas and PyTorch, graded by real tests in an isolated sandbox.
System design
10 design cases. Sketch the architecture on a canvas, write your reasoning, then check which key components your design covers.
Mock interviews
15 original prompts with follow-ups. Answer with a timer running, then review your answer against a checklist.
Real interview questions
77 questions paraphrased from real hiring conversations, each labelled by company type, role and year.
Project deep dives
5 original project briefs, and a private place to connect each implementation decision to evidence you can explain.
BUILT TO MAKE PRACTICE STICK
A plan, a review loop and a record of your progress.
Guided paths
8 paths that put questions, labs and primary-source reading in order, module by module.
Preparation plan
Set your target role, interview date and weekly hours, paste the job description and get a list of what to practise next.
Spaced review
Questions and labs you miss come back after a day, then after 3, 7 and 14 days as you get them right.
Scored assessments
6 short checks that mix questions and labs, so you can see where you stand before the real thing.
Playlists and courses
9 focused practice sets and 4 short written courses for going deeper on one area.
Progress record
Streaks, XP, a 13-week activity map and a count of what you can solve without hints.
Practice arena
3 quick drills on token recall, tensor shapes and relevance ranking, a few minutes each.
Field notes
6 short articles on design answers, RAG debugging, leakage audits and project stories.
TWO TRACKS. SHARED FIRST PRINCIPLES.
Retrieval. Agents. Evaluation.
Build reliable products around models. Develop judgement on context, safety, latency, cost and measurable quality.
Start free Free · no card neededData. Models. Production.
Connect statistical understanding to dependable systems. Explore features, training, serving, monitoring and drift.
Start free Free · no card neededQUESTIONS
Frequently asked questions
Is The Agentic Line free?
Yes. Every question, coding lab, design case and mock interview prompt is free to use. A free account saves your progress, and you can try sample questions without one.
What does an applied AI engineer interview cover?
Loops vary by company, but most combine concept questions on retrieval, agents, evaluation and model behaviour with a Python coding round, a system design round and a discussion of a past project. The practice here is organised around those rounds.
How is an ML engineering interview different?
ML engineering interviews lean further into data, modelling and production: validation and leakage, training and generalisation, ranking, deployment and monitoring. The ML Engineering track covers those, and both tracks share an engineering foundations track.
How are the coding labs graded?
Your Python runs in an isolated sandbox against the lab’s tests, including hidden ones, and you see how many passed. The ML labs include NumPy, pandas and PyTorch (CPU).
Do I need to install anything?
No. Questions, coding labs, the design canvas and mock interviews all run in the browser.
Where do the real interview questions come from?
Each one is paraphrased from a real technical hiring conversation, mostly for AI and ML engineering roles. Names of companies, people and products are removed; each question keeps only the type of company, the role and the year.