LLMs or ML First?
The Answer Depends on You.

Yes, for most practical applications in 2026. If your goal is building AI-powered products, you can be highly effective using LLMs as powerful APIs without understanding backpropagation. Most AI engineers at startups spend their time on prompt engineering, RAG systems, and integrations, not training models from scratch. You can always backfill ML theory later if you hit the ceiling.

ML-first makes sense if: 1) You want to do AI research or work at research labs, 2) You're interested in training or fine-tuning models from scratch, 3) You want to work on non-language AI (computer vision, robotics, recommendation systems), 4) You're genuinely curious about the math and theory. If none of these apply, LLM-first is likely faster to value.

Absolutely. Many successful AI engineers started with high-level tools and backfilled theory as needed. Starting with LLMs gives you quick wins and momentum. When you hit limitations or want deeper understanding, you'll have context for why the theory matters. Learning is not a one-way door.

The majority of AI engineering roles in 2026 are about building with LLMs, not training models. Companies want people who can ship: RAG systems, agents, tool use, evaluations, production deployments. Deep ML knowledge is valued but not required for most positions. The bottleneck is practical experience, not theoretical depth.

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.

A coaching session cuts through months of analysis paralysis. In one conversation, we map your specific background, goals, and constraints to a clear learning path. No more Reddit debates or YouTube rabbit holes. You walk away knowing exactly what to learn, in what order, and why it's right for you.

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.