Are AI Certifications Worth It?
The Real Answer.

It depends on your target role. For cloud AI/MLOps: AWS Machine Learning Specialty or Google Professional ML Engineer carry weight. For enterprise AI: Azure AI certifications matter if targeting Microsoft shops. For pure AI engineering roles: certifications matter far less than GitHub portfolio and project experience. The only certs worth pursuing are those that directly map to job requirements you're seeing in postings for your target role.

Portfolio first, always. Here's the reality: a certification proves you passed a test. A portfolio proves you can build. When hiring managers review candidates, they spend seconds on certifications but minutes on portfolio projects. Build 2-3 substantial AI projects that solve real problems, then consider adding 1-2 strategic certifications as validation. Never the reverse.

It varies wildly. Large enterprises and government contractors often require specific certifications for compliance reasons—in those cases, they're table stakes. Startups and tech companies typically don't care and may even view cert-heavy resumes with skepticism (suggests theory over practice). Research your target employers specifically rather than assuming certs help universally.

Most professional AI certifications require 40-80 hours of preparation for experienced developers, 100-200+ hours for career changers. That's 1-3 months of evenings and weekends. The question isn't whether you have the time—it's whether that time is better spent building portfolio projects that demonstrate the same skills while creating tangible assets you can show employers.

Cert stacking is usually a trap. Three certifications doesn't make you 3x more hireable—it often signals insecurity about your actual skills. One well-chosen certification plus a strong portfolio beats five certifications with no practical work. The exception: if pursuing enterprise architect roles where specific cert combinations are explicitly required.

They can help career changers establish baseline credibility, but they're not enough alone. A certification tells employers you understand concepts; it doesn't prove you can apply them. Career changers should use certifications strategically: get one foundational cert early for confidence, then immediately start building projects. The project work is what will actually land interviews.

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.

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.