What Tools Do
AI Engineers Use?
The core stack: Python, LangChain, vector databases like Pinecone, and cloud platforms.
But knowing which tools to learn first saves you months of wasted effort.
AI Native Engineer Community Access
The AI Tool Landscape Is Overwhelming.
You Don't Know What to Prioritize.
New AI frameworks launch every week. LangChain, LlamaIndex, Haystack, CrewAI. You have no idea which ones employers actually care about.
You spent months learning a tool that became obsolete or that no one uses in production. Time wasted on the wrong stack.
Job postings list 15+ tools as requirements. You feel like you need to learn everything before you can even apply.
Learn the Tools Employers Actually Want
The AI Career Accelerator
I've worked at GitHub and reviewed hundreds of AI engineering portfolios. Most tools in job postings are nice-to-haves. Master the core stack, and you're qualified for 80% of roles. Here's what actually matters.
Master the Core Stack
Python, LangChain, vector databases, basic cloud deployment
Build Production Projects
Use tools in real applications, not just tutorials
Learn on the Job
Specialized tools are learned once you're hired
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.
The AI Tool Ecosystem Moves Fast. Focus Beats Breadth.
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 are the essential tools for AI engineers in 2026?
The core stack: Python (non-negotiable), an orchestration framework like LangChain or LlamaIndex, a vector database (Pinecone, Weaviate, or Qdrant), API integration skills, and basic cloud deployment (AWS, GCP, or Azure). Master these and you're qualified for most AI engineering roles. Everything else is learned on the job.
Do I need to learn LangChain for AI engineering?
LangChain is the most requested framework in AI job postings, so yes, learn it. But understand the concepts behind it: chains, agents, memory, retrieval. Frameworks change, but the patterns remain. Once you know LangChain well, picking up alternatives like LlamaIndex takes days, not months.
Which vector database should I learn?
Start with Pinecone or Qdrant. They're popular in job postings and have good documentation. The specific database matters less than understanding how embeddings and similarity search work. Once you know one vector DB, switching to another is straightforward. Focus on building RAG systems that actually work.
Do I need prior AI experience?
Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.
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.
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.
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.
What cloud platforms do AI engineers need to know?
AWS is most common, followed by GCP and Azure. You don't need certification-level knowledge. Focus on: deploying containerized apps (ECS, Cloud Run), basic infrastructure (S3, API Gateway), and managed AI services (Bedrock, Vertex AI). Enough to deploy your projects, not enough to be a cloud architect.
How do I avoid tool overwhelm in AI engineering?
Focus on principles over specific tools. Learn one orchestration framework deeply, one vector DB, one cloud platform. Build projects that combine them. When job postings list tools you don't know, most are learnable in a week if you have solid fundamentals. Companies hire for problem-solving ability, not tool checkbox completion.
Ready to Land Your AI Role?
Stop watching others succeed. Start building your AI career today.
30-minute strategy call • Limited spots available