What Languages Do
AI Engineers Need?
The short answer: Python is essential, JavaScript helps with web integration, Go and Rust are optional for performance work.
But learning languages in the wrong order wastes months of your time.
You Know You Need Programming Languages.
But Which Ones Should You Learn First?
You keep switching between Python tutorials, JavaScript courses, and Rust guides. Analysis paralysis is real and you have made no meaningful progress.
You spent months learning a language that turned out to be irrelevant for AI work. Now you are starting over with Python and feeling behind.
Job postings list 5-7 languages as requirements. You have no idea if they actually need all of them or if it is just wish list padding.
Here's the Language Priority Stack for 2026
The World-Class AI Engineer Cohort
I've hired AI engineers and reviewed hundreds of job applications. Here's what companies actually require versus what is nice to have.
Master Python First
95% of AI work happens in Python. Get solid here before anything else.
Add JavaScript for Web Integration
Build frontends and APIs for your AI systems to be production-ready.
Optional: Go or Rust for Performance
Only if targeting infrastructure or performance-critical roles.
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.
While You Debate Which Language to Learn, Others Are Building AI Systems
Frequently Asked Questions
Is Python alone enough for AI engineering?
For most AI engineering roles, yes. Python dominates the AI ecosystem: LangChain, LlamaIndex, HuggingFace, OpenAI SDK, and most AI frameworks are Python-first. You can build complete AI systems, including APIs with FastAPI, without touching other languages. Add more languages only when specific roles require them.
Do I need JavaScript for AI engineering?
It depends on your target roles. For backend AI engineering, Python is sufficient. For full-stack AI products, JavaScript or TypeScript helps you build user interfaces and integrate with web applications. Many AI startups want engineers who can build end-to-end, which means Python for AI logic and JavaScript for frontends.
When should I learn Go or Rust for AI work?
Only after you are job-ready with Python. Go is useful for building high-performance APIs and microservices. Rust matters for inference optimization and systems-level AI infrastructure. These are specializations, not requirements. Most AI engineers never need them.
How long to learn Python well enough for AI engineering?
4-8 weeks for someone with any programming background. 8-12 weeks for complete beginners. Focus on practical Python: functions, classes, APIs, async programming, package management. You do not need to be a Python expert. Intermediate skills combined with AI domain knowledge is what employers want.
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.
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'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.
What about Java, C++, or other languages for AI?
Java appears in enterprise AI roles, especially at large corporations. C++ matters for ML infrastructure and model optimization, not typical AI engineering. SQL is technically a language and very useful for data work. But none of these should be your starting point. Python first, always.
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.