AI Interview Common Mistakes:
What Gets Candidates Rejected

Top coding mistakes: (1) Jumping into code without clarifying requirements—always ask questions first, (2) Silent coding—you must explain your thinking, even if it feels awkward, (3) Not testing—run through examples before saying you're done, (4) Giving up on hard problems—partial progress with clear thinking beats silence, (5) Over-engineering—sometimes brute force is fine to start, (6) Ignoring edge cases—null inputs, empty arrays, negative numbers, (7) Bad variable names—'x' and 'temp' hurt readability. The pattern: most mistakes are about process and communication, not raw coding ability.

System design red flags: (1) Jumping to components without understanding requirements—start with clarifying questions, (2) Going too deep too fast—establish the high-level architecture first, (3) Ignoring scale requirements—'how many users?' matters for your design, (4) No trade-off discussion—every design decision has pros and cons, (5) Forgetting operational concerns—monitoring, failure modes, cost, (6) Over-engineering for AI roles—not every problem needs distributed systems, (7) Not drawing diagrams—visual communication is part of the evaluation. System design tests structured thinking and communication as much as technical knowledge.

Behavioral red flags: (1) Vague answers—'I worked with the team' vs specific actions you took, (2) No STAR structure—rambling stories lose the interviewer, (3) Taking all the credit—'I' is fine but acknowledge team contributions, (4) Only positive stories—failure stories with learning show self-awareness, (5) Badmouthing previous employers—even if justified, it looks bad, (6) Not preparing enough stories—scrambling to find examples mid-interview, (7) Stories that don't match the question—listen carefully to what's being asked. Practice 10-15 stories and map them to common behavioral questions.

AI interview red flags: (1) Not knowing your own projects deeply—you built it, you should explain every decision, (2) Overstating capabilities—saying your RAG system had 95% accuracy without metrics to back it up, (3) Confusing AI hype with reality—know the limitations of current LLM technology, (4) No production experience—if all your AI work is notebooks, that's a gap, (5) Can't explain trade-offs—why did you choose GPT-4 over Claude? Why this embedding model?, (6) No cost awareness—production AI has real cost implications, (7) Ignoring evaluation—how did you measure if your system actually works?

Universal interview mistakes: (1) Poor time management—arriving late (even 1 minute) starts you negative, (2) No questions for the interviewer—signals low interest, (3) Appearing desperate—desperation repels, confidence attracts, (4) Not researching the company—basic questions about what they do are embarrassing, (5) Negative body language—crossed arms, no eye contact, fidgeting, (6) Over-talking—concise answers beat rambling ones, (7) Under-preparing—'winging it' rarely works for competitive roles, (8) Not following up—a thank-you email is still expected.

Some mistakes matter more for career changers: (1) Over-apologizing for lack of experience—focus on transferable skills instead, (2) Not having AI projects—even personal projects count, build something, (3) Theory without implementation—reading papers doesn't replace building systems, (4) Underselling adjacent experience—software engineering, data, research skills transfer. Your lack of AI titles matters less than your ability to demonstrate you can do the work.

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.