MLOps Engineer Jobs:
Your Realistic Path Into AI
You don't need a PhD to break into AI.
MLOps engineers earn $95K-$200K+ using skills that transfer directly from DevOps and software engineering.
AI Native Engineer Community Access
You Want Into AI, But the Math PhD Route Isn't Realistic.
There's Another Way In.
ML Engineer roles want PhDs. 36% list it as preferred. You're competing against 5-year academics.
You know Docker, CI/CD, and cloud platforms. But you don't know how to leverage them for AI roles.
The MLOps market is exploding ($16.6B by 2030) but you're not sure how to position yourself.
Your DevOps Skills Are Your Ticket Into AI.
The AI Career Accelerator
MLOps is DevOps plus machine learning knowledge. You don't need to implement algorithms from scratch. You need to deploy, monitor, and scale ML systems. If you know Docker, Kubernetes, and cloud infrastructure, you're already halfway there.
Audit Your Stack
Map your Docker, CI/CD, and cloud skills to MLOps
Add ML Tooling
Learn MLflow, Kubeflow, model serving, and monitoring
Land MLOps Roles
Position your engineering background for $120K+ offers
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.
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.
The MLOps Talent Gap Is Your Opportunity
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 skills do MLOps engineers need?
MLOps engineers live in Docker, Kubernetes, Terraform, and Airflow. Docker appears in 59% of Kubernetes-focused job listings. The key difference from ML Engineering: every one of these tools was built to be learned. Terraform has interactive sandboxes, Kubernetes has free environments where you learn by breaking things. These tools came from the DevOps world where self-taught engineers are the norm.
What do MLOps engineers earn?
Entry-level MLOps roles pay $95K-$115K, similar to ML Engineer positions. Senior roles hit $150K-$200K+. While you won't see the extreme $500K+ compensation at elite AI labs that ML researchers command, the path to six figures is much more accessible. Your projects matter more than your degree.
Can I transition from DevOps to MLOps?
DevOps to MLOps is the most natural transition in the AI space. You already have most of the skills: Docker, CI/CD, cloud platforms, infrastructure as code. You just need to add ML-specific tooling on top (model versioning, experiment tracking, feature stores, and model monitoring). The systems thinking you've developed translates directly.
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.
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.
Do I need ML experience to become an MLOps engineer?
You need to understand ML concepts, but you don't need to implement algorithms from scratch. You need to know what a model does, not how to build one from pure math. Most MLOps engineers come from software development or DevOps backgrounds rather than data science. If you understand systems engineering (containers, orchestration, cloud infrastructure), you can learn the ML-specific pieces on top.
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