AI Technical Screen Preparation:
Pass the Technical Filter

Typical AI technical screens include: (1) A coding problem—usually Python, often involving APIs or data processing, (2) AI concept questions—explain RAG, embeddings, or prompt engineering, (3) Discussion of your projects—be ready to go deep on technical details. Some companies include mini system design. The bar is lower than onsite—they're checking fundamentals, not advanced mastery.

Most use: (1) Shared editor (CoderPad, HackerRank) for live coding, (2) Your own IDE with screen share, (3) Whiteboard for system design discussions. For live coding: practice in the specific tool if possible, but any shared editor practice helps. Have your environment ready: Python installed, common libraries available, editor configured. Test your screen share before the interview.

Common AI questions: Explain how RAG works and when you'd use it, What are embeddings and how are they generated?, Walk me through building an LLM-powered feature, How would you handle rate limits in production?, What's the difference between fine-tuning and prompting?, How do you evaluate LLM output quality? Prepare crisp 2-3 minute explanations for each.

Common failures: (1) Diving into code without clarifying requirements, (2) Silent coding—explain your thinking, (3) Perfect-is-enemy-of-good—get a working solution first, (4) Not testing your code with examples, (5) Ignoring edge cases until asked, (6) Defensive reactions to hints or corrections. Interviewers want to see how you think and collaborate, not just whether you get the answer.

For a 45-minute screen: 5 minutes for clarification and approach, 25-30 minutes for coding, 5-10 minutes for testing and optimization, 5 minutes for questions. Don't spend too long on the approach—start coding within 10 minutes. If stuck, communicate it: 'I'm considering two approaches...' Interviewers often give hints if you're on track but slow.

You should be comfortable: calling LLM APIs, processing JSON responses, basic text/data manipulation in Python, explaining AI concepts clearly. A few months of hands-on project experience is usually sufficient. Practice by building small projects and explaining your code to others. If you can build a simple RAG system and explain each component, you're ready.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.