Senior Software Developer with 20 years of experience designing and building scalable applications, distributed systems, architectures, and complex workflows. Proficient in JavaScript and TypeScript, with a strong background in frontend(React, NextJS)
As a child, I was always fascinated by computers. At the age of 20, I decided to pursue a career as a professional software developer. I began taking C++ courses and passed the Plovdiv University (PU) entrance exam with a score of 5.40. However, shortly after that, I moved to Sofia and started ...
When I was 20-25 years old, I loved running. My long-distance achievement was running 'The Belgrade Ultramarathon' - 118 km for 24 hours. I have always played sports. This is extremely important to me. It helps me discipline myself and concentrate better. Sports definitely improve ...
Constantly striving for self-development; Responsibility, precision and loyalty to the position; Organization of the work process, teamwork;Rapid assimilation of information and, accordingly, its adequate implementation in the working position;Creative and innovative ...
AI isn't happening to you. You have control. When cloud computing emerged, system administrators feared their jobs would disappear. Some jobs did change. But the engineers who survived and became more valuable were the ones who learned how cloud works and improved their skills to match the new reality. We've seen this before—with the calculator, the assembly line. Each time, the ones who adapted thrived. The same pattern is happening with AI. Focus on three things: learn how AI models work on a basic level, experiment with AI tools in your current work, and build skills AI cannot do—understanding business problems, communicating with non-technical people, making judgment calls about architecture. The DeepSeek moment proved that AI is democratizing; the competitive advantage is no longer who has the fanciest tools but who understands the problems they're solving.
Many engineers feel pressured to become AI experts overnight, skipping the basics. That's not what companies want. Look at DevOps and cloud job descriptions—few mention AI tools. What they need are engineers who understand fundamentals, integrate AI where it makes sense, and know when AI is not the solution. That skill doesn't get replaced by AI; it becomes more valuable. You have control over how you use AI and what skills you build. AI is a tool—it amplifies what you bring. If you only know how to write code, you become less valuable. If you understand problems, communicate with people, and make good decisions about architecture and trade-offs, AI makes you more powerful. The engineers who thrive use AI to write code faster so they can spend more time on what matters: understanding business value, designing systems, and making decisions that require context.
The difference between irreplaceable engineers and those worried about AI often comes down to learning order. Most engineers jump straight to how—how to use the tool, how to write the code. They rarely spend time on what it does or why it exists. AI is especially strong at the how; given a clear problem, it can implement common patterns quickly. That's why engineers who only know the how are exposed. The engineers who stay valuable follow What → Why → How. Before opening documentation, they ask: what problem does this solve, why does this solution exist, what did people do before. Answering these first makes the how easier to learn and builds understanding that AI can't replace. Understanding problems is harder than implementing solutions—that's where human value sits. Companies care most about engineers who can frame problems, explain trade-offs, and decide when something is worth building.
Whether you're a developer, data scientist, or AI enthusiast, this session gives you the practical foundation to start building smart, dynamic AI solutions.
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