System Administrator to AI Engineer
Your Infrastructure Skills Are Gold.
You've mastered Linux, automation, and infrastructure.
Those skills are exactly what AI teams are desperately hiring for.
The Transition Feels Impossible.
Programming skills gap feels huge. Shell scripts aren't Python, and ML code looks like another language entirely.
ML theory seems overwhelming. Neural networks, transformers, backpropagation—where do you even start?
Career perception works against you. 'Just a sysadmin' doesn't sound like AI engineer material on paper.
Your Ops Skills Are the Shortcut.
The World-Class AI Engineer Cohort
Here's what most sysadmins don't realize: AI teams need people who can deploy, scale, and maintain ML systems in production. That's infrastructure. That's you. The path isn't learning ML from scratch—it's positioning your existing skills for the AI industry.
Enter Through MLOps
Your bridge to AI—deploy models, not build them
Add Python Fluency
8-12 weeks of focused learning, not years
Position & Land
Sell infrastructure expertise to AI teams
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 Infra People Now
Frequently Asked Questions
What sysadmin skills transfer to AI engineering?
More than you think. Linux expertise is essential—most ML runs on Linux servers. Your automation skills (Ansible, Terraform, scripting) translate directly to MLOps pipelines. Container orchestration (Docker, Kubernetes) is core to model deployment. Monitoring and logging experience applies to model observability. Networking knowledge matters for distributed training. You're not starting from zero—you're starting from 60%.
What is MLOps and why is it the best entry point?
MLOps is DevOps for machine learning—deploying, monitoring, and maintaining ML models in production. It's the perfect bridge because it leverages what you already know (infrastructure, automation, reliability) while exposing you to ML workflows. Companies desperately need MLOps engineers because data scientists can build models but often can't deploy them reliably. You solve that problem. From MLOps, you can expand into ML engineering if you want, or specialize deeper in infrastructure.
How hard is the Python learning curve for sysadmins?
Easier than you expect. You already think programmatically from shell scripting. Python is more readable and has better tooling. Focus on: Python basics (2-3 weeks), data manipulation with pandas (2 weeks), ML frameworks basics (3-4 weeks), and deployment tools like FastAPI and MLflow (2-3 weeks). The total is 8-12 weeks of focused learning, not the years of CS background some bootcamps suggest you need.
How long does the transition realistically take?
For a motivated sysadmin: 4-6 months to land your first MLOps or AI infrastructure role. Month 1-2: Python fluency and ML basics. Month 3-4: MLOps tools (Kubeflow, MLflow, model serving). Month 5-6: Portfolio projects and job search. This assumes 10-15 hours per week alongside your current job. With full-time focus, you could compress this to 2-3 months.
What salary increase can I expect?
Senior sysadmins typically earn $90K-$130K. MLOps engineers and AI infrastructure specialists command $140K-$200K+ in 2026 markets. The premium exists because supply is scarce—most AI talent wants to build models, not deploy them. Your operations background is rare in AI, making you valuable. Expect 30-60% salary increases for comparable experience levels.
Do I need an ML degree or certification?
No. For MLOps and AI infrastructure roles, hands-on experience beats credentials. What matters: proven infrastructure skills (you have these), Python proficiency (learnable), familiarity with ML tools (Kubeflow, MLflow, model serving), and a portfolio showing you can deploy and maintain ML systems. Cloud certifications (AWS ML Specialty, GCP ML Engineer) can help but aren't required. Focus on demonstrable skills over paper credentials.
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.
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.
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.