How to Become an
MLOps Engineer
The bridge between data science and production.
MLOps Engineers ensure ML models actually work in the real world—earning $140K-$240K+.
Want to Be the Person Who Makes
ML Models Actually Work in Production?
Data scientists build models, but 85% never make it to production. MLOps engineers close that gap.
ML systems have unique deployment challenges: model drift, data quality, experiment tracking. DevOps alone isn't enough.
Companies are learning that ML infrastructure is as important as the models. MLOps demand is exploding.
The MLOps Engineering Path
The World-Class AI Engineer Cohort
MLOps Engineers combine DevOps fundamentals with ML-specific knowledge. Here's the roadmap to becoming an MLOps specialist.
Master DevOps Fundamentals
CI/CD, containers, Kubernetes, infrastructure as code, monitoring
Learn ML Concepts
Model training, evaluation, data pipelines—enough to work with data scientists
Build MLOps Expertise
MLflow, feature stores, model registries, experiment tracking, ML monitoring
Develop Production Skills
Model serving, A/B testing, drift detection, pipeline orchestration
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.
85% of ML Models Never Reach Production. MLOps Engineers Fix That.
Frequently Asked Questions
What exactly is MLOps Engineering?
MLOps (Machine Learning Operations) Engineering focuses on deploying, monitoring, and maintaining ML systems in production. You're the bridge between data scientists who build models and the production systems that serve predictions. Key responsibilities: building ML pipelines, managing model deployments, monitoring model performance, tracking experiments, managing feature stores, ensuring reproducibility, and automating ML workflows. It's DevOps for machine learning—with ML-specific challenges like data drift and model degradation.
How is MLOps different from DevOps?
MLOps extends DevOps with ML-specific concerns. DevOps deploys code—MLOps deploys code plus data plus models. Unique MLOps challenges: model versioning (not just code versioning), data drift monitoring, feature stores, experiment tracking, model registries, A/B testing for models, and reproducibility. DevOps engineers can transition to MLOps, but they need to learn ML-specific concepts and tools. The infrastructure skills transfer; the ML knowledge needs to be added.
What skills do I need for MLOps?
DevOps foundation: CI/CD (GitHub Actions, Jenkins), Docker, Kubernetes, cloud platforms (AWS/GCP/Azure), Terraform/IaC, monitoring (Prometheus, Grafana). MLOps-specific: MLflow, Kubeflow, experiment tracking, model registries, feature stores, model serving (TensorFlow Serving, Triton), data versioning (DVC). ML concepts: understanding of model training, evaluation metrics, data pipelines (don't need to build models, but need to understand them). Python proficiency is essential.
How long does it take to become an MLOps Engineer?
From DevOps/SRE: 3-5 months to add ML-specific knowledge. From data science: 4-6 months to develop strong DevOps fundamentals. From software engineering: 6-9 months to learn both DevOps and ML concepts. From scratch: 12-18 months. The fastest path is from DevOps—you already have the infrastructure skills. Build 2-3 end-to-end ML pipeline projects to demonstrate capability.
What salary can MLOps Engineers expect?
Entry-level: $120K-$160K. Mid-level: $160K-$200K. Senior: $200K-$260K+. Staff/Principal: $250K-$350K+. MLOps salaries are comparable to or slightly higher than DevOps because of the added ML complexity. The role is in high demand as companies mature their ML practices. Consulting rates range from $150-$300/hour for independent MLOps specialists.
What does an MLOps Engineer do day-to-day?
Typical activities: building and maintaining ML pipelines, debugging model deployment issues, setting up experiment tracking, monitoring model performance and data drift, working with data scientists to productionize their models, managing infrastructure for model training and serving, automating repetitive ML workflows, ensuring reproducibility and compliance. Less coding than data science, more infrastructure and automation. Lots of troubleshooting why things don't work in production.
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 should I invest in learning?
15-20 hours per week for 4-6 months if you have DevOps or engineering background. Focus on building complete ML pipelines—not just studying tools. Each project should cover: data processing, model training pipeline, model serving, monitoring. The learning is in solving real integration problems, not just following tutorials.
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.