Technical Support to AI Engineer
Your Path Forward.

Be realistic: 12-24 months for most people. Unlike developers who already code, you're building programming fundamentals from scratch while working full-time. The first 6 months focus heavily on Python and computer science basics. Months 6-12 involve ML fundamentals and your first projects. Months 12-24 are about landing a stepping-stone role and gaining engineering experience. Rushing this creates gaps that will hurt you in interviews and on the job.

Perception - both others' and your own. You've spent years being categorized as 'non-technical' even though you debug complex issues daily. Hiring managers don't automatically see support experience as engineering-relevant. You need to actively reframe your experience: troubleshooting IS debugging, user escalations ARE requirements gathering, documentation IS technical communication. Build a portfolio that proves your engineering capabilities beyond your job title.

You could go direct, but it's significantly harder. Stepping-stone roles like Support Engineer, DevOps, or ML Ops give you three things: 1) Engineering experience on your resume, 2) Time to learn while getting paid, 3) Internal mobility opportunities. A 12-month stint as a DevOps engineer makes your AI engineer application much stronger than going from support directly. That said, if you build an exceptional portfolio and network effectively, direct transitions do happen.

No. AI engineering is a new enough field that there's no established 'typical' background. Your years of user-facing experience are actually valuable - most engineers lack that perspective. What matters is demonstrable skills: can you code, do you understand ML concepts, and can you ship projects? Age bias exists in tech, but it's less pronounced in AI where domain expertise and mature judgment have clear value. Focus on building skills, not worrying about demographics.

Expect 10-15 hours per week of focused study. Early mornings before work and weekends are most effective for most people. Start with Python fundamentals (3 months), then data structures and algorithms (2 months), then ML basics (3 months). Use your support role strategically: automate parts of your job with Python, analyze ticket data, build internal tools. This creates portfolio pieces while demonstrating initiative to your current employer.

Quality over quantity. 3-4 strong projects beat 10 weak ones. Include: 1) An end-to-end ML project (data collection to deployment), 2) Something that solves a real problem you encountered in support, 3) A project demonstrating software engineering skills (testing, CI/CD, documentation), 4) Contributions to open source if possible. Each project should have a clear README explaining the problem, your approach, and results. GitHub activity and a technical blog also help.

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.