From online learning to landing a full-time AI Engineer role — all in just one year. Levi Garcia’s journey is proof that with focus, the right resources, and a growth mindset, real-world success in AI is within reach. We sat down with Levi to uncover how he turned curiosity into career momentum — and transformed learning into impact.
🧠 What sparked your interest in AI and data science?
I’ve always been passionate about science and technology. Toward the end of my undergraduate studies, I became more aware of the growing opportunities in data science and AI. The field felt dynamic, practical, and full of real-world impact. I went on to pursue a Master’s in Information Processing Science — essentially data science — but wanted to deepen my skills even further. That’s when I came across the Machine Learning A–Z course on Udemy, which led me to discover SuperDataScience.
🧑🏫 What made SuperDataScience stand out from other learning platforms?
The interactivity. I also tried Codecademy to improve my Python skills, but with SuperDataScience, the combination of theory, code, projects, and live labs really clicked for me. The learning felt more grounded and connected to the real world.
📚 How did you structure your learning journey?
At first, I explored everything: CNNs, LLMs, image processing. But once I saw what employers were looking for, I narrowed my focus. Most job listings emphasized LLMs and AI rather than traditional analytics, so I pivoted. I followed the Data Science Career Path on SDS at first, and eventually leaned fully into AI and LLM content, especially anything related to real-world implementation.
💬 What role did the SDS community and live labs play?
Huge. I attended live labs with Luka — he introduced me to tools like Hugging Face — and participated in a collaborative project led by Shaheer that focused on S&P 500 prediction using LLMs. At first, I was intimidated; some team members already had industry experience. But they were supportive, and I learned a lot from how they approached problems. That real-world teamwork helped me build confidence and gave me a project I now revisit for inspiration.
💼 Tell us about landing your first AI Engineer job.
I applied to a ton of jobs — everything from data analyst to machine learning engineer — but most roles mentioned LLMs. When I saw the job posting for AI Engineer at a company called Busy (they help people start businesses), I felt ready.
I passed the first round, then prepared for the technical interview by revisiting the SDS course on fine-tuning LLMs. That refresher helped me confidently explain how to build an LLM that could answer questions using a company’s internal data.
They told me they were impressed, especially with my understanding of both fundamentals and practical implementation. A few days later, I got the job.
🤖 What are you working on now?
I’m part of a data team of eight. My role is to build internal AI tools — for example, a chatbot that helps stakeholders query our large data warehouse. Right now, I’m improving the agents that power the chatbot to make sure they generate consistent, accurate SQL queries.
Do you want to hear the exciting part? I’ve been trusted to lead the implementation — restructuring their prototype, turning notebooks into modules, and proposing improvements. It’s a huge opportunity, and I’m determined to grow into it.
🚀 How does SDS still support your growth?
Learning doesn’t stop once you land a job. Now that I’m solving real problems, I use SDS differently. I revisit projects, browse new live labs, and explore topics like agents, which I’m now working with daily. The depth of the content — and the ability to reach out for help in the community — keeps me moving forward.
🔑 What advice would you give to others breaking into AI?
Patience and persistence. It’s easy to get discouraged when others seem more experienced. But if you keep learning and improving every day, opportunities will come. I worked hard for this role, and it paid off. Also, don’t be afraid to collaborate or ask for help. Growth often happens through others.
💬 What would you say to someone thinking of joining SuperDataScience?
If you want to break into AI, this is where you should be. The content is practical, the mentors are world-class, and the community truly helps each other grow. SuperDataScience doesn’t just teach you — it prepares you to deliver real-world value.
Want to follow in Levi’s footsteps? Start your journey with SuperDataScience.