From Data Scientist
to AI Engineer
You build models that work in notebooks.
Now learn to ship them to production.
Your Models Work.
They Just Never Ship.
Your Jupyter notebooks are full of working models that never reach production.
You know ML theory cold, but deployment, APIs, and infrastructure feel foreign.
You hand off models to engineers who rebuild everything from scratch.
Your ML Skills Are the Hard Part. Engineering Is Learnable.
The World-Class AI Engineer Cohort
Data scientists have the foundation AI teams need most: statistics, ML algorithms, and model development. The gap is production engineering: APIs, deployment, monitoring, and scale. With your background, this gap closes faster than you think.
Production Foundations
Learn APIs, containers, and deployment basics
MLOps Essentials
Model serving, monitoring, and pipelines
Ship Real Systems
Build end-to-end AI applications that run in production
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.
ML Knowledge Is Your Unfair Advantage
Frequently Asked Questions
Why are data scientists well-positioned for AI engineering?
Data scientists already understand the core of AI systems: ML algorithms, statistics, feature engineering, and model evaluation. Most software engineers transitioning to AI need months to learn what you already know. Your gap is narrower: add production skills like API development, containerization, and deployment. In 2026, companies want engineers who can build AND deploy ML systems.
What production skills do data scientists need for AI engineering?
Priority order: (1) API development with FastAPI or Flask to serve models, (2) Containerization with Docker for reproducible deployments, (3) Basic cloud infrastructure on AWS, GCP, or Azure, (4) Model monitoring and observability in production. You do not need to become a full DevOps engineer. Focus on the 20% of engineering that handles 80% of production ML use cases.
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.