Landing a role in AI at a top tech company like Google is no quick feat—it took me an entire year of daily dedication and continuous learning to make that leap. But here’s where it gets really interesting: the journey wasn’t just about mastering technical skills; building a personal brand through content creation played a huge role in my success. And this is the part most people miss when they think about transitioning into AI roles.
When I embarked on my career as a software engineer, AI wasn’t as prominent as it is today—back in 2019, it was a niche for PhD candidates and specialists. My dad, also in tech, kept encouraging me to explore AI, but I simply brushed it off, focusing on getting my footing in software engineering. Fast forward to 2023, the tech world shifted dramatically. Suddenly, AI was everywhere, and I realized it was time to pivot. I committed myself to acquiring AI knowledge and started sharing what I learned through social media, which surprisingly helped establish my presence and credibility in the field.
It’s important to understand the distinction between roles in AI: an AI machine learning engineer is primarily involved in creating and training models, while an AI software engineer integrates these models into applications and builds the supporting infrastructure. My switch to an AI role on Google's Workspace AI team was the result of deliberate effort—devoting around two hours each day for several months to courses and projects, while developing content around these concepts helped reinforce what I learned. The process wasn’t instantaneous; it took about seven months from the start of my upskilling to landing the new position.
For anyone looking to make a similar transition, here’s a strategy that worked for me: first, engage deeply with foundational AI concepts through reputable courses—Google’s internal training and Coursera are great starting points. But don’t just passively consume material. Explaining complex ideas out loud or through creating content forces a deeper understanding. Feedback from an audience or peers pushes you even further. If content creation isn’t your thing, focus on hands-on projects and consistently follow AI developments and news to demonstrate your evolving expertise.
The application process inside Google was less traditional since it was an internal move—I wasn’t subjected to the usual external interviews but still had to showcase my skills and growth through my résumé, especially highlighting side projects. This part was crucial in validating my transition. So, while it’s true that mastering AI skills takes serious time and effort, combining technical learning with personal branding and practical projects can dramatically improve your chances. What do you think—could building a personal brand be the missing piece for more tech career shifts? Let me know your thoughts!
