While everyone around me worried about AI threatening their job security, I took a completely different approach. I decided to turn this technological revolution into my biggest career advantage – and it worked better than I could have imagined. I want to share how I went from a complete beginner to a six-figure Senior Engineer at big tech in just four years.

My Unconventional Career Journey

My story isn’t what you’d typically expect. I didn’t have special connections or advantages when I started. At 20 years old, I was independently learning software development and AI while studying full-time. I didn’t follow a traditional college curriculum – I leveraged online resources and focused intensely on practical skills.

By 21, I’d secured an internship at Microsoft as a junior customer engineer. At 22, I made what seemed like a risky move – quitting Microsoft to become an Azure DevOps engineer for more hands-on experience. By 23, I joined big tech as a medium software engineer, and at 24, I was promoted to senior software engineer.

In essence, I condensed what would typically be a 10+ year career journey into just four years. And here’s the thing: I believe you can do this too.

What Actually Accelerated My Career

The single most important factor that accelerated my growth wasn’t natural talent or luck – it was learning how to solve real business problems with modern AI.

As I gained more experience implementing AI solutions at big tech, I noticed something fascinating: while many engineers could talk about AI concepts or build simple prototypes, very few could actually bring AI solutions from concept all the way to production. This gap became my opportunity.

When I started tracking my income growth, I was shocked. I almost tripled my income since starting as a new grad, reaching six figures faster than I ever expected. But something even more important happened – I developed a career that’s actually resilient to the changes AI will bring to the job market.

I realized that as AI might take over more traditional jobs, the people implementing AI systems will remain essential. This isn’t just a job – it’s a long-term investment in my future security.

Breaking My Own Limiting Beliefs

The biggest obstacles I faced weren’t technical – they were psychological. I had to overcome beliefs like:

• “I’m not ready for an AI role”
• “I can’t earn six figures with my current skills”
• “I can’t learn this complex field quickly enough”

These limiting beliefs almost stopped me from applying to positions that seemed “beyond my experience.” What I discovered is that companies are desperately seeking people who can implement AI solutions, not just understand the theory.

When I shifted my mindset from “I’m not ready” to “they’re lucky to have me,” everything changed. I started proving my worth through solving real problems that delivered measurable value.

Why I Created This Community

After reaching senior level at big tech, I realized something important: I cannot clone myself to take on 10 jobs at once, but I can empower others to follow a similar path. That’s why I created this community.

Unlike many educators who create courses without actual industry experience, I’m actively building AI solutions in production every day. I understand both the technical implementation and the business context that makes these projects valuable.

When community members tell me they’ve landed their first AI role or received a significant promotion, it’s genuinely the most rewarding part of my work. I believe that by sharing what worked for me – the exact roadmap I followed – I can help you achieve similar results in your own career.

The Skills That Actually Matter

Through my journey, I’ve discovered that what separates the engineers who advance quickly from those who progress at a conventional pace isn’t theoretical knowledge – it’s these practical abilities:

Production Implementation: I learned to move beyond proof-of-concept to deliver fully functioning AI systems that operate at scale.

Business Value Orientation: I focused on understanding how my implementations translated to measurable business outcomes and ROI.

Full-Stack AI Knowledge: Rather than specializing too narrowly, I developed proficiency across the entire AI implementation stack.

Impact Measurement: I learned to quantify and communicate the business value of my work, directly connecting it to organizational success.

These are precisely the skills I teach in our community, because they’re what actually helped me advance.

If you’re interested in becoming a high paid AI engineer, join the AI Engineering community where we share insights, resources, and support for your learning journey.