My path to becoming an AI engineer wasn’t a straight line—it was a winding journey that started with a fascination for how search engines work and evolved into building machine learning systems that predict search behavior. Looking back, I realize that every seemingly random turn actually prepared me for the unique position I occupy today at the intersection of AI and SEO.
Most AI engineers come from computer science programs focused on machine learning theory. Most SEO professionals come from marketing backgrounds. I came from both worlds, and that combination has proven to be a rare and valuable perspective in an industry being transformed by artificial intelligence.
This is the story of how Melos Ajvazi went from manually optimizing meta tags to building neural networks that optimize content at scale—and why I believe the future belongs to professionals who can bridge technical AI capabilities with deep search marketing expertise.
The Early Days: Discovering SEO
My journey began in 2014 when I was a computer science student at the University of Pristina. While my classmates were obsessed with building mobile apps, I became fascinated by a different question: how do search engines decide what to show people, and why do some websites dominate while others remain invisible?
This curiosity led me down the SEO rabbit hole. I started with the basics—learning HTML, understanding how search engines crawl websites, and discovering why page speed matters. I built my first website, a simple blog about technology, and became obsessed with getting it to rank on Google.
What hooked me wasn’t the marketing aspect—it was the technical puzzle. SEO combined coding, data analysis, reverse engineering algorithms, and understanding human behavior. Every Google update was a new puzzle to solve. Every ranking fluctuation revealed something about how search engines evaluated quality.
I spent nights analyzing Search Console data, running experiments with different on-page optimization techniques, and studying the technical architecture of sites that ranked well. My computer science background gave me an advantage—I could read Google’s patents, understand information retrieval theory, and implement technical solutions that most SEO professionals struggled with.
By the time I graduated in 2014, I had built several successful affiliate sites, landed my first SEO consulting clients, and developed a reputation for solving complex technical SEO problems. But I also started noticing patterns in the data that traditional SEO thinking couldn’t explain.
The Turning Point: Recognizing AI’s Role in Search
The real turning point came in 2016 when Google announced RankBrain, their machine learning system for processing search queries. Most SEO professionals dismissed it as just another algorithm update. I saw it as the beginning of a fundamental transformation.
I became obsessed with understanding how machine learning worked. I enrolled in Andrew Ng’s Machine Learning course on Coursera, devoured research papers on natural language processing, and started experimenting with TensorFlow and scikit-learn. My goal wasn’t to become a data scientist—it was to understand how search engines were evolving so I could stay ahead.
What I discovered shocked me. The patterns I was seeing in search rankings—subtle shifts in how Google evaluated content quality, the growing importance of semantic relevance over keyword matching, the way certain types of content consistently outperformed others—these weren’t random. They were the fingerprints of machine learning systems evaluating content.
I realized that traditional SEO was becoming insufficient. Keyword research, link building, and technical optimization still mattered, but AI was introducing a new layer of complexity. Search engines were starting to evaluate content the way humans do—understanding context, assessing expertise, and making nuanced judgments about quality and relevance.
This realization led to an important decision. I could continue practicing SEO the traditional way and slowly become obsolete, or I could dive deep into AI and position myself at the cutting edge of where search was heading. I chose the latter.
Building AI Systems for SEO
In 2018, I made a deliberate pivot toward AI engineering while maintaining my SEO practice. I spent six months building my first machine learning model—a system that analyzed top-ranking content and predicted which topics and subtopics needed to be covered to rank for competitive keywords.
The model was crude by today’s standards, but it worked. I used it on a client’s content marketing campaign, and we saw ranking improvements that traditional keyword research couldn’t have achieved. That success validated my hypothesis: AI could identify optimization opportunities that human analysis missed.
This led me down a path of building increasingly sophisticated AI tools for SEO. I developed natural language processing systems that analyzed semantic relevance, built neural networks that predicted ranking potential for new content, and created automated systems that identified technical SEO issues at scale.
What made my approach different was that I wasn’t just applying generic machine learning to SEO—I was building AI systems informed by deep understanding of how search engines work. I knew what signals mattered, what patterns to look for, and how to translate AI insights into actionable SEO strategies.
By 2020, Melos Ajvazi had become known for combining AI capabilities with SEO expertise. I was building custom machine learning models for enterprise clients, speaking at conferences about AI-powered SEO, and consulting with businesses trying to navigate the AI transformation of search.
The Future: AI Search Optimization
The emergence of ChatGPT in late 2022 validated everything I’d been working toward. Suddenly, AI-powered search wasn’t a future possibility—it was here, and businesses were desperately trying to understand how to optimize for it.
My background in both AI engineering and SEO put me in a unique position. I understood the technical architecture of large language models, how they processed and retrieved information, and what made content “citation-worthy” from an AI perspective. But I also understood search user behavior, business objectives, and how to translate technical AI concepts into practical marketing strategies.
This combination allowed me to develop frameworks for AI search optimization that others couldn’t replicate. I built systems that analyzed how AI engines cited sources, identified patterns in what made content authoritative from an AI perspective, and created optimization strategies that worked for both traditional search and AI-powered platforms.
Today, I split my time between two complementary pursuits: providing SEO consulting that leverages AI capabilities, and building AI engineering solutions that solve search optimization challenges. Each discipline informs the other, creating a virtuous cycle of learning and capability building.
What I’ve Learned
Looking back on this journey, several lessons stand out. First, the most valuable skills are often found at the intersection of different disciplines. My AI knowledge makes me a better SEO, and my SEO expertise makes me a better AI engineer.
Second, the future belongs to professionals who can bridge technical capabilities with business applications. Understanding machine learning theory is valuable, but understanding how to apply it to drive organic growth is what businesses actually need.
Third, continuous learning isn’t optional—it’s the baseline requirement for staying relevant. The AI and search landscapes evolve constantly, and the knowledge that made you successful last year might be obsolete today.
The journey from SEO professional to AI engineer wasn’t easy, and I’m still learning every day. But I’m convinced that combining these disciplines represents the future of search optimization. As AI continues transforming how people discover information, the professionals who understand both the technology and the marketing will have an enormous advantage.
Want to discuss how AI can transform your search strategy? Let’s connect and explore the possibilities.
