How I’d Prepare for an AI Engineer Interview in 30 Days

If I had an AI Engineer interview coming up in 30 days, I wouldn’t try to learn everything about AI. Instead, I’d focus on the skills interviewers can really assess: Python, machine learning basics, LLMs, RAG, AI agents, system design, coding, projects, and behavioral questions.

AI and ML interviews have changed. Today, they often mix coding and ML basics with topics like LLMs, RAG, agents, system design, and real-world issues such as evaluation, speed, cost, and handling failures.

In this article, I’ll explain how to get ready for an AI Engineer interview in 30 days.

Prepare for an AI Engineer Interview in 30 Days

Here’s how I would organize those 30 days.

Week 1: Python, Coding, and ML Fundamentals

I’d use the first week to review the basics:

1. Day 1: Python – functions, classes, data structures, and list or dictionary operations. Practice three easy coding problems.
2. Day 2: Python – focus on NumPy and Pandas. Practice filtering, grouping, joins, handling missing values, and data transformations.
3. Day 3: SQL – work on SELECT, JOIN, GROUP BY, subqueries, and window functions. Practice three to five SQL problems.
4. Day 4: Supervised vs. unsupervised learning, regression, classification, clustering.
5. Day 5: Training/validation/test sets, cross-validation, overfitting, underfitting, and bias-variance tradeoff.
6. Day 6: Model evaluation. Practice explaining precision, recall, F1 score, ROC-AUC, and situations where accuracy can be misleading.
7. Day 7: Mock interview. Choose fifteen ML questions and answer them out loud without checking your notes.

A common mistake is preparing for ML interviews like they’re university exams. Interviewers want to see how you think through problems, not just if you can repeat definitions. That’s also the idea behind my book, Cracking Your First AI/ML Interview.

Week 2: LLMs, RAG, and AI Agents

In the second week, I’d focus more on modern AI engineering topics:

1. Day 8: Study the basics of transformers, tokens, embeddings, attention, and context windows.
2. Day 9: Learn about prompting, structured outputs, function calling, hallucinations, temperature, and how to choose models.
3. Day 10: Get a full overview of RAG, including document ingestion, chunking, embeddings, vector databases, retrieval, reranking, and generation.
4. Day 11: Practice answering questions like: Why does RAG hallucinate? How can you improve retrieval quality? How would you evaluate a RAG system? When would you fine-tune instead of using RAG?
5. Day 12: Study the basics of AI agents, including tool calling, planning, memory, state, orchestration, and handling failures.
6. Day 13: Design an AI agent for customer support. Consider tools, permissions, memory, guardrails, evaluation, and observability.
7. Day 14: Build or review a small RAG or agent project, and get ready to explain each architectural choice you made.

This is important because AI system-design interviews now often include RAG and agent scenarios. Interviewers will test your understanding of reliability, evaluation, safety, and operational trade-offs.

Week 3: System Design and Your Projects

I would take this week especially seriously:

1. Day 15: Learn the basic ML system-design framework: requirements, data, model, training, serving, evaluation, and monitoring.
2. Day 16: Design a recommendation system.
3. Day 17: Design a fraud detection system.
4. Day 18: Design a RAG-based customer-support system.
5. Day 19: Design an AI agent that can use multiple tools.
6. Day 20: Study production concerns: latency, scalability, caching, cost, monitoring, data drift, model failures, and rollback.
7. Day 21: Take a deep dive into your projects.

Pick your two best projects and get ready to explain:

1. What problem were you solving?
2. Why did you choose this approach?
3. What alternatives did you consider?
4. What failed?
5. How did you evaluate it?
6. What would you change in production?

I always tell learners that the project round is a great chance in an interview because you picked the project. The interviewer might know the technology, but you should know your own work better than anyone.

Week 4: Interview Simulation

In the final week, focus less on learning new topics and more on practicing under real interview conditions:

1. Day 22: 5 Python coding problems.
2. Day 23: 5 SQL problems and 10 rapid-fire ML questions.
3. Day 24: LLM/RAG mock interview.
4. Day 25: AI agent mock interview.
5. Day 26: Two complete system-design questions.
6. Day 27: Behavioral preparation.

Prepare answers for:

1. Tell me about yourself.
2. Why AI engineering?
3. Tell me about your best project.
4. Tell me about a technical failure.
5. Tell me about a difficult team situation.
6. How do you handle deadlines?
7. Tell me about something you had to learn quickly.

For days 28, 29, and 30, focus on light review. Don’t try to learn anything major. Go over your notes, projects, resume, and the questions you found difficult.

The Book You Can Use Throughout the 30 Days

I wrote Cracking Your First AI/ML Interview specifically around this problem.

It’s not just a list of answers to memorize. Each question is organized to show what the interviewer is testing, how to think about the problem, and how to handle similar questions you haven’t seen before.

The book covers ML fundamentals, data preprocessing, regression and classification, tree-based models, Python, SQL, NumPy, Pandas, LLMs, RAG, AI agents, and project discussions.

I’d use it in the first three weeks to review concepts and practice questions, then spend the last week finding the areas where I still need work.

The Takeaway

Thirty days is enough time to make real progress, but only if you stop treating interview prep as passive studying.

Don’t spend four hours watching videos and call it preparation. Solve problems, explain concepts out loud, design systems on paper, rebuild your projects, and practice with a timer.

When you don’t know an answer, don’t just memorize it right away. First, try to understand why the interviewer asked the question. That’s the mindset I want you to build.

I hope you found this article on preparing for an AI Engineer interview in 30 days helpful. For more tips on AI and machine learning, feel free to follow me on Instagram.