AI Skills Gap Analysis
For Career Changers.

A skills gap analysis compares your current abilities against the actual requirements for AI roles you're targeting. It's not about listing every AI topic, it's about identifying the specific gaps between where you are and where you need to be. A proper analysis considers your background, target role, and the fastest path to becoming hireable.

Most people overestimate what they know and underestimate what they need. Start by listing what you can actually build, not what you've watched tutorials on. Can you build an ML pipeline from scratch? Deploy a model to production? Debug a transformer architecture? If you're guessing, that's a gap. Working with someone experienced helps cut through self-deception.

In 2026, the biggest gaps are usually: 1) Production ML systems, not just notebooks, 2) LLM application development and prompt engineering, 3) ML infrastructure and deployment, 4) Data engineering fundamentals, 5) Software engineering best practices applied to ML. Career changers often have theoretical knowledge but lack hands-on building experience.

It depends entirely on your starting point and target role. A software developer pivoting to ML engineering might need 3-6 months of focused effort. Someone from a non-technical background might need 12-18 months. The key word is focused. Random learning takes 2-3x longer than targeted gap-closing with clear milestones.

Self-assessment has a blind spot problem. You don't know what you don't know. A coach who's actually working in AI can identify gaps you'd miss, validate your self-assessment, and help you prioritize ruthlessly. They've seen what actually gets people hired versus what sounds impressive but doesn't matter.

Yes, but your skills gap will be larger and take longer to close. You'll need to build programming fundamentals before tackling AI-specific skills. The honest assessment matters even more, many non-technical career changers underestimate the foundation required. A realistic timeline prevents burnout and wasted effort on advanced topics you're not ready for.

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.