From DevOps Engineer
to AI Engineer
You deploy systems at scale. You automate everything.
Now add AI to your toolkit and multiply your value.
AI Native Engineer Community Access
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 AI Career Accelerator
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
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 Infrastructure People Who Get AI
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
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 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.
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 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.
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.
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