From DevOps Engineer
to AI Engineer
You deploy systems at scale. You automate everything.
Now add AI to your toolkit and multiply your value.
You Build Infrastructure.
But AI Feels Like a Black Box.
AI is transforming every company, and you feel left behind without ML knowledge.
You see AI engineer roles but the machine learning math looks intimidating.
Junior engineers with AI skills are getting offers while your DevOps experience gets overlooked.
Your DevOps Skills Are 60% of AI Engineering.
The World-Class AI Engineer Cohort
AI engineers need infrastructure experts who can deploy models, scale systems, and build reliable pipelines. You already know Docker, Kubernetes, CI/CD, and monitoring. The AI layer is smaller than you think. Let's build on your foundation.
Audit Your Skills
Map Docker, K8s, CI/CD to AI workflows
Add AI Fundamentals
LLM APIs, vector DBs, AI agents
Build and Deploy
Ship production AI systems that scale
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 Teams Need Infrastructure People Who Get AI
Frequently Asked Questions
Why are DevOps engineers well-suited for AI engineering?
AI engineering is mostly production engineering. Building AI systems requires containerization, orchestration, CI/CD pipelines, monitoring, and scaling. DevOps engineers already master these. The gap is understanding LLMs, vector databases, and AI-specific patterns. That takes weeks to learn, not years. Companies want engineers who can ship AI to production, not just run notebooks.
What do DevOps engineers need to learn for AI roles?
Focus on applied AI, not theory. Core additions: LLM APIs (OpenAI, Anthropic, local models), vector databases (Pinecone, Weaviate), AI orchestration frameworks (LangChain, LlamaIndex), and AI agent patterns. Your Docker, Kubernetes, and monitoring skills transfer directly. Skip the PhD math. Learn to build production AI systems.
What is the difference between AI Engineer and MLOps Engineer?
MLOps focuses on model training pipelines, experiment tracking, and model serving infrastructure. AI Engineering focuses on building AI-powered applications using LLMs, agents, and retrieval systems. DevOps skills apply to both, but AI Engineering is often the faster path if you want to build products rather than maintain training infrastructure.
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.