AI Portfolio Presentation Interview:
Showcase Your Work Effectively

Select projects that: (1) Demonstrate AI-specific skills (RAG, LLM integration, embeddings), (2) Show end-to-end implementation, not just notebooks, (3) Have measurable outcomes or clear use cases, (4) You can explain deeply and confidently. Quality over quantity—2-3 strong projects beat 10 superficial ones. At least one should involve production concerns like deployment or error handling.

For each project (5-8 minutes): (1) Problem—what you were solving, (2) Approach—why you chose this architecture, (3) Technical decisions—key choices and trade-offs, (4) Challenges—what was hard and how you solved it, (5) Results—what you achieved or learned, (6) Reflections—what you'd do differently. Interviewers remember stories better than feature lists.

Match depth to audience. Start high-level, then offer to dive deeper. Say: 'I used a hybrid search approach—would you like me to explain why?' This shows you can adapt communication. Be ready for deep dives on: model selection rationale, architecture decisions, performance trade-offs, error handling approaches. If you can't explain a decision, reconsider including that project.

If you don't know: 'I haven't explored that approach, but here's how I'd investigate it.' If you made a mistake: 'In retrospect, I'd do X differently because Y.' If it's outside scope: 'That wasn't a focus for this project, but here's how I'd approach it.' Honesty and thoughtfulness matter more than having all answers. Never bluff—interviewers can tell.

Live demos are high-risk, high-reward. If you demo: (1) Have a backup recording in case of failures, (2) Test everything immediately before, (3) Keep it short—2-3 minutes max, (4) Have a script for what you'll show. Alternatively, use recorded demos or screenshots. A smooth recording beats a nervous live demo that breaks. Focus on explaining, not performing.

Personal projects are perfectly valid. Present them professionally: describe the problem you solved, the users (even if hypothetical), technical decisions, and outcomes. What matters is demonstrating your skills and thought process, not the project's origin. Many successful AI engineers were hired based on impressive personal projects.

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.