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.
AI Native Engineer Community Access
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 AI Career Accelerator
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
When I started in tech, I was based in the Netherlands with no connections and only thousands of video game hours under my belt. Not exactly the ideal starting point.
My first tech job was software tester. One of the most junior roles you can start with. I was just happy someone took a chance on me.
I kept learning. Kept pivoting. But what actually accelerated my career wasn't more certifications or more code. It was learning to solve problems that matter and proving beyond a doubt that what I built solved real problems. That's the skill that stays future-proof, even with AI.
I've since worked remotely for international software companies throughout my career. Proof that the high-paid remote path is possible for anyone with the right skills and motivation. In the end, I went from a $500/month internship to 6 figures as a Senior AI Engineer at GitHub.
Now I teach over 22,000 engineers on YouTube. Becoming an AI-Native Engineer is a system I lived through and offer to you today.
Real Results
Vittor
AI Engineer
Landed his first AI Engineering role in 3 months
"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
Personalized Roadmap & Career Strategy
A custom plan tailored to your background, goals, and timeline. No generic advice.
Weekly 1:1 Coaching Calls
Direct access to Zen for guidance, project feedback, and answers to your questions.
Portfolio-Ready AI Projects
Build production-grade AI applications to showcase to employers. Work that gets you hired.
Interview Prep & Mock Interviews
Practice technical and behavioral interviews. Learn what hiring managers look for.
Resume & LinkedIn Optimization
Transform your online presence to attract recruiters. Stand out from other applicants.
Community Career Support
Join the AI Native Engineer community. Not seeing results yet? You stay and keep going. We're with you through the ups and downs.
AI Teams Need Infra People Now
Every month you delay can cost you thousands in lost earning potential. While you're watching tutorials, others are landing $120K+ AI Engineering roles.
I can only work with a limited number of 1:1 clients at a time to ensure you get the personalized attention you deserve.
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.
Do I need prior AI experience?
Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.
How much time do I need to commit?
Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.
What if I don't land interviews in 90 days?
You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.
How is this different from online courses?
Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.
What's the investment for 1:1 coaching?
Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.
Can I do this while working full-time?
Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.
Ready to Land Your AI Role?
Stop watching others succeed. Start building your AI career today.
30-minute strategy call • Limited spots available