How to Become an
AI Platform Engineer
Build the foundation others build on.
AI Platform Engineers create the infrastructure that enables AI at scale—earning $150K-$250K+.
AI Native Engineer Community Access
Want to Build AI Infrastructure
Instead of Applications?
You prefer building platforms over products. The infrastructure that enables AI applications is more interesting than the applications themselves.
AI platform engineering requires unique skills: GPU orchestration, model serving, and scale that traditional platform work doesn't demand.
Companies are building internal AI platforms to standardize ML operations. Platform engineers who understand AI are in high demand.
The AI Platform Engineering Path
The AI Career Accelerator
AI Platform Engineers combine platform engineering skills with ML systems knowledge. Here's how to build this infrastructure-focused specialization.
Master Platform Engineering
Build foundation in Kubernetes, cloud infrastructure, and DevOps
Learn ML Infrastructure
Understand model serving, GPU management, and ML pipelines
Build Self-Service Systems
Create platforms that let data scientists deploy models easily
Optimize for Scale
Handle thousands of requests per second with cost efficiency
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 Applications Need Platforms. Platform Engineers Who Understand AI Are Rare.
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 does an AI Platform Engineer actually do?
AI Platform Engineers build the infrastructure that AI applications run on. Common projects: internal ML platforms for model deployment, GPU cluster management, model serving infrastructure (scaling, routing, caching), feature stores, experiment tracking systems, and MLOps pipelines. You enable data scientists and AI engineers to deploy and operate models without infrastructure expertise. The focus is reliability, scalability, and developer experience.
What skills do I need for AI platform engineering?
Platform fundamentals: Kubernetes, Docker, cloud services (AWS/GCP/Azure), IaC (Terraform). ML-specific: model serving (Triton, vLLM, TGI), GPU management, CUDA basics, ML frameworks (PyTorch, TensorFlow). Systems: distributed systems, caching, load balancing, monitoring. API design: creating good abstractions for internal users. You need deep infrastructure knowledge plus enough ML understanding to build tools ML engineers actually want to use.
What do AI Platform Engineers earn?
Entry-level: $130K-$160K (2-3 years platform experience). Mid-level: $160K-$200K (4-6 years). Senior: $200K-$260K (7+ years). Staff/Principal: $250K-$350K+. AI platform roles at major tech companies often include significant equity. This is one of the higher-paying AI specializations because it requires both deep infrastructure expertise and ML understanding. Contract rates: $150-$250/hour.
How is AI Platform different from AI Application engineering?
AI Platform engineers build for other engineers. You're creating abstractions, APIs, and infrastructure that make AI development easier. AI Application engineers build for end users—chatbots, recommendations, analysis tools. Platform work is more about reliability, scalability, and developer experience. Application work is more about user features and business logic. Platform engineers typically have deeper infrastructure backgrounds.
How do I start in AI Platform Engineering?
Path 1: From platform/DevOps engineering, add ML infrastructure skills. Learn model serving, GPU basics, ML pipelines. Path 2: From ML engineering, go deeper into infrastructure. Learn Kubernetes, distributed systems, cloud architecture. Build projects that demonstrate both: deploy a model serving system with autoscaling, create an experiment tracking platform, set up GPU cluster management. Contribution to open-source ML infrastructure projects is highly valued.
What technology do AI Platform Engineers use?
Orchestration: Kubernetes, Ray, KubeFlow. Model serving: vLLM, Triton Inference Server, TGI, BentoML. Experiment tracking: MLflow, Weights & Biases, custom solutions. Feature stores: Feast, Tecton, custom builds. Pipelines: Airflow, Prefect, Dagster. Monitoring: Prometheus, Grafana, custom ML metrics. Cloud: AWS SageMaker, GCP Vertex AI, Azure ML (understanding, not necessarily using). The stack depends on company scale—startups use managed services, large companies build custom platforms.
What background do I need for AI platform engineering?
Ideal backgrounds: Platform engineer, DevOps engineer, SRE, or infrastructure engineer wanting to specialize in AI. You need solid Kubernetes and cloud infrastructure experience as foundation. Some ML/AI understanding is required but you don't need to be an ML expert—you need to understand enough to build tools for people who are. Backend engineers with systems interest can transition but need to learn infrastructure depth.
How long does it take to become an AI Platform Engineer?
From platform/DevOps engineer: 4-6 months to add ML infrastructure skills. From ML engineer: 6-9 months to deepen infrastructure knowledge. From backend engineer: 9-12 months (learn infrastructure fundamentals, then AI platform specifics). This role requires genuine depth in both areas—there are no shortcuts. Building and deploying actual ML infrastructure projects is the best way to demonstrate competence.
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