Database Administrator to AI Engineer
Your Data Skills Are Your Edge.
DBAs understand data better than most engineers ever will.
That's exactly what AI engineering needs.
Stuck in the 'Non-Developer' Box?
You write SQL all day but get labeled as 'not a real programmer' by hiring managers.
ML concepts feel overwhelming when your background is in relational databases, not algorithms.
AI roles want Python and machine learning experience, but your resume screams 'operations.'
Bridge the Gap, Don't Start Over.
The World-Class AI Engineer Cohort
You already understand data at a level most AI engineers never reach. Data modeling, query optimization, pipeline architecture - these are foundational AI skills. The transition isn't about starting over. It's about adding ML tools to your existing expertise.
Reframe Your Experience
Position data skills as AI foundations
Add Python & ML Basics
Targeted learning, not a full bootcamp
Build AI + Data Projects
Showcase your unique combination
Meet Your Mentor
My aim has been the same for years: become a world-class AI engineer. Every career move I've made has been measured against that.
I started as a software tester on a $500/month internship in the Netherlands. Taught myself to code, learned to ship real systems, and worked my way to Senior Engineer at GitHub.
Then I left GitHub. I joined an AI research lab as Member of Technical Staff, where I currently build products for secure AI monitoring.
The cohort draws directly from my real experience so you can make progress fast.
I run this special cohort with only a few people because hands-on work with me is what it takes to bring you to become a world-class AI engineer.
Real Results
Vittor
AI Engineer
Built and deployed his portfolio piece, then landed the AI role
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
8 Weekly Tuesday Sessions
3 hours each for 24 live hours total.
Project Scoping at Kickoff
We set the scope of what you'll ship and the milestones to get there before the live sessions start.
Code Reviews
Reviews of your code from Zen during the cohort.
Lifetime Demo Access
Every architecture demo is recorded and yours to keep.
Demo Day
You present what you built and get feedback from Zen, with a recording you can use in your portfolio.
12 Months Community Access
Included with the cohort.
AI Needs Data Experts Who Actually Understand Data
Frequently Asked Questions
What advantages do DBAs have in AI engineering?
DBAs bring critical skills most AI engineers lack: deep understanding of data architecture, experience with production-scale systems, intuition for data quality issues, and performance optimization expertise. In 2026, as AI systems move from prototypes to production, companies desperately need people who understand both ML and robust data infrastructure. Your background positions you for specialized roles like ML Data Engineer or AI Platform Engineer.
How hard is Python if I only know SQL?
Easier than you expect. SQL and Python share logical thinking patterns - you're used to manipulating data, just with different syntax. Libraries like pandas even use SQL-like operations (joins, filters, groupby). Most DBAs become comfortable with Python data manipulation within 4-6 weeks. The bigger learning curve is understanding ML concepts, not the programming itself.
Am I too senior/experienced to make this transition?
Your seniority is an asset, not a liability. AI teams need people who understand production systems, can debug complex data issues, and know how to work with large-scale infrastructure. Junior ML engineers often lack these skills. Position yourself for senior AI/ML Data Engineering roles where your experience commands premium compensation.
Will I take a pay cut transitioning to AI?
Unlikely in 2026. Senior AI/ML roles typically pay $150K-$250K+, often more than DBA positions. Your data expertise combined with ML skills is a rare combination. Target roles like ML Platform Engineer, AI Data Engineer, or MLOps Engineer where your background is particularly valuable. These roles often pay more than pure ML researcher positions.
What job titles should I target?
Consider: ML Data Engineer, AI Platform Engineer, MLOps Engineer, Machine Learning Infrastructure Engineer, or AI Solutions Architect. These roles value your database and infrastructure experience alongside ML skills. Avoid entry-level 'ML Engineer' titles that undervalue your experience - target mid-senior roles that leverage your full background.
How much time will this take?
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.
I've signed up for cohorts before and dropped out. How is this different?
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.
I'm not pivoting careers. I want to build a product. Does this still work?
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.
Do I need prior AI experience?
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.
What does it cost?
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.
Can I do this while working full-time?
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.
I accept those who have the highest chance of success.
In the 30-minute call we discuss your goals and whether you are ready for the program.