From Data Engineer
to AI Engineer in 90 Days
Your pipelines already feed the models other people get paid to build.
Learn to build and ship what consumes your data and earn what they earn.
You Move the Data.
Someone Else Moves Up.
The AI engineers querying your warehouse take home 30 to 50 percent more than you do.
You ship reliable pipelines at scale, but you have never served a model or wired up embeddings yourself.
Every AI tutorial assumes you are starting from zero, so you keep relearning data basics you already mastered.
Your Foundation Is Closer to AI Than Almost Anyone's.
The World-Class AI Engineer Cohort
Data engineers already own the hardest parts of production AI: data quality, scale, orchestration, and monitoring. ML engineers usually fight those exact problems. The path here is not a rebuild. It is adding model serving, embeddings, vector stores, and LLM integration on top of skills you already use every day.
Map What Transfers
Pinpoint which pipeline, SQL, and infra skills count directly toward AI roles
Add the ML Layer
Feature stores, model serving, vector databases, and RAG pipelines built on your existing stack
Reposition and Land
Present yourself as the AI infrastructure expert teams are short on and interview from strength
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.
Data Engineers Have a Window to Claim AI Roles Before Everyone Else Retrains
Frequently Asked Questions
Why are data engineers well positioned to become AI engineers?
AI systems are data systems first. You already own data quality, pipeline reliability, SQL and transformations, cloud infrastructure, and production monitoring. These are the exact areas where many ML engineers struggle. Your remaining gap is narrow: embeddings, vector databases, model serving, and LLM integration. That is why a data engineer often ramps into AI faster than a self taught beginner.
What should a data engineer learn first to move into AI engineering?
Work in this order. First, vector databases and embeddings, which extend the database skills you already have. Second, feature stores and ML pipelines, which mirror the data pipelines you build now. Third, model serving, which is deploying models behind APIs. Fourth, RAG systems, which combine your data strengths with LLMs. You do not need to start with deep math. Focus on the engineering layer of AI.
Do I need a machine learning degree to transition from data engineering to AI?
No. AI engineering roles reward people who can ship reliable systems, not people who can derive gradients by hand. Your value is production data experience that most ML hires lack. Coaching focuses on the applied skills hiring managers test for so you can demonstrate real AI capability without going back to school.
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.