Portfolio Projects Employers
Actually Want

Employers look for three things: 1) Real problem-solving - projects that solve actual pain points, not tutorial exercises, 2) Production quality - proper documentation, error handling, testing, and deployment, 3) Technical depth appropriate to your target role - showing you understand why you made certain choices, not just that you copied code. A single well-architected project that solves a genuine problem beats ten tutorial clones.

Quality matters infinitely more than quantity. Two or three excellent projects demonstrating real problem-solving will outperform a dozen tutorial clones. Focus on depth: one end-to-end ML system with proper data pipelines, monitoring, and documentation shows more than five Jupyter notebooks with sklearn examples.

Tutorials are fine for learning, but they shouldn't be your portfolio. Everyone has done the same sentiment analysis and image classifier tutorials. If you include tutorial-based projects, extend them significantly: add unique features, deploy them properly, handle edge cases the tutorial ignored. Better yet, use tutorial skills to solve an original problem.

This is exactly where portfolio projects matter most. Focus on: 1) Open source contributions to real projects people use, 2) Personal projects that solve problems you actually face, 3) Kaggle competitions with detailed write-ups explaining your approach, 4) Freelance or volunteer work for nonprofits needing ML solutions. The key is demonstrating you can operate independently and ship real solutions.

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.

Building in isolation means you don't know what you're missing. Coaching provides: 1) Insider perspective on what hiring managers actually evaluate, 2) Project ideas based on current industry needs, 3) Code review that catches issues before employers see them, 4) Strategic guidance on which projects to prioritize for your target role. Most candidates waste months on projects that don't demonstrate the right skills.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

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.