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.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.