From Cloud Engineer
to AI Engineer
You provision, scale, and secure cloud platforms every day.
Layer AI on top of that foundation and become the engineer every team wants.
You Run the Infrastructure AI Lives On.
Yet the AI Roles Keep Going to Other People.
You can architect a multi-region platform, but you have never shipped an LLM feature, so AI postings feel out of reach.
Provisioning the same VPCs, IAM policies, and Terraform modules has started to feel repetitive while the interesting AI work goes to others.
AI engineering salaries climb 20 to 40 percent above pure cloud roles, and you are watching that gap widen without a clear way in.
Your Cloud Platform Is Where Real AI Gets Deployed.
The World-Class AI Engineer Cohort
Every production AI system runs on compute, storage, networking, and managed services that you already control. Hiring managers are short on engineers who understand both the cloud platform and the AI layer that sits on it. You bring the infrastructure half already. We add LLM APIs, retrieval, and agent patterns so you can ship complete AI systems, not just host them.
Translate Your Cloud Stack
Map your AWS, GCP, or Azure expertise to where AI systems actually need it
Add the AI Layer
Learn LLM APIs, vector stores, and orchestration that run on your cloud
Ship and Position
Deploy a production AI project and frame your cloud depth as a hiring advantage
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.
Teams Are Hunting for Cloud Engineers Who Can Also Build AI
Frequently Asked Questions
Why do cloud engineers make strong AI engineers?
Production AI is a cloud problem before it is a model problem. Real AI applications need compute optimization, storage for embeddings, secure networking for API traffic, IAM, and auto-scaling. Cloud engineers already own EC2 and GCE, S3 and GCS, VPCs, and managed services. The only gap is the AI layer: LLM behavior, vector databases, and retrieval patterns. That gap takes weeks, not years. Companies want people who can ship AI on real infrastructure, not just demo it in a notebook.
What does a cloud engineer need to learn to move into AI?
Focus on applied AI that runs on the cloud you already know. The core additions are LLM APIs such as OpenAI, Anthropic, Bedrock, and Vertex AI, vector databases such as Pinecone, Weaviate, and pgvector, orchestration frameworks such as LangChain and LlamaIndex, and agent design patterns. You can skip the heavy research math. Your networking, security, and deployment skills carry straight over, so the learning curve is far shorter than most cloud engineers expect.
Which cloud AI services should I start with as a cloud engineer?
Begin with the managed AI services on your primary cloud so you build on familiar ground. On AWS that means Bedrock for models and SageMaker for serving. On GCP that means Vertex AI and Cloud Run. On Azure that means the Azure OpenAI Service and Azure ML. Then learn the cloud-agnostic patterns: containerized AI apps, vector database integration, and clean API design. Your existing certifications gain real weight once they sit alongside shipped AI work.
How much time will this take?
You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.
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.
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.