By: Joddi S
In 2010, Daniil Mazepin, a fourth-year mathematics student at a Ukrainian university, made a decision that made little sense on paper: he would build software for the iPhone, a device whose operating system he had never used, and for which he couldn’t yet write a single line of code. Daniil Mazepin had no way of knowing that this one stubborn choice would set the course of the next fifteen years.
Trained as a mathematician, Mazepin gravitated toward the layer of technology that most people never see and rarely think about: the engineering and infrastructure that keep digital products running reliably at scale. It is unglamorous work, and it became his specialism. Over the following years, he became a world-class engineering leader responsible for the dependability of products used by millions, and, most recently, for the infrastructure powering a new generation of artificial intelligence.
Today Daniil is an Engineering Manager at CoreWeave, among the handful of companies laying the computational foundations of the AI era, where he leads the technical squads architecting the foundational systems that bring artificial intelligence into physical reality. “Being here feels like standing exactly where the next decade of technology is being poured,” he says. “The scale is enormous, and there’s almost no room for things to break, which happens to be the kind of problem I’ve spent my whole career learning to solve,” he recalls.
For all the industries he has passed through, mobile, gaming, finance, now AI, Mazepin says the connecting thread was never something he set out to find. “I never planned to become the reliability person,” he admits. “But wherever I ended up, the same lesson kept surfacing: a product is only worth as much as people’s confidence that it won’t let them down.” Where does that instinct come from? Partly from chess. Mazepin competed seriously as a boy, collecting titles, and the pull he felt toward the board’s puzzles was the very thing that later drew him to mathematics, two expressions of one appetite for structure. His aptitude won him a place on the Applied Mathematics and Computer Science course at Donetsk National University, among the strongest tech students of its kind in the country. That road could easily have carried him deep into pure theory. An iPhone pulled him elsewhere. “The moment I held one, I knew that was the world I wanted to build in,” he remembers. “I had no idea how any of it worked underneath, but wanted to work that out”
In 2015, as iOS Lead, he joined one of the biggest gambling providers in the world: Playtech. There, he quickly rose to Tech Lead, running engineering for the entire mobile casino division. The obstacle waiting for him there was as commercial as it was technical: Apple’s limits on app size meant only a fraction of Playtech’s game library could fit on an iPhone, and without the full library, the company couldn’t seriously contest the United States, the industry’s richest market. Mazepin’s solution was a ground-up rethink of how each game was packaged, shrinking each title’s footprint by more than ninety per cent and finally letting the whole catalog fit inside a single iOS app. That work opened America to the company. “Once real money is moving through a system every second, you stop treating stability as something you bolt on,” he says. “It turns into the entire job, and that’s stayed with me in everything I’ve built since.”
That kind of result doesn’t go unnoticed. Meta sought him out, bringing him to London to help lead toward the global launch of Shops, a retail ecosystem designed for massive scale. From there, he was headhunted by Teya, a British fintech unicorn, where Daniil headed the team building its Dynamic Currency Conversion platform, multiplying eligible transaction volume tenfold and, for the first time, opening the capability to tens of thousands of small and medium-sized businesses across Europe.
Mazepin’s contribution to his field has never stopped at the edge of his job description. A Senior Member of IEEE since 2023, a standing that only a small fraction of members reach, he also sits on its evaluation panels, weighing whether fellow engineers merit that same senior distinction. And he has become a widely read voice on the theme his career keeps circling back to: how to build systems people can actually depend on. His writing on availability, service-level objectives, and the link between reliability and user experience has appeared in some of technology’s most-read outlets: HackerNoon, which ranked him among its leading contributors; DOU, the largest engineering community in Eastern Europe; and AI Journal, where his analysis of what will define the next generation of successful AI companies. Daniil’s conclusion rarely wavers, and it echoes what his own work keeps proving: lasting technology rests on trust and reliability, not on hype.
That same preoccupation with trustworthy systems has drawn him, well out of the spotlight, into the engineering behind Pairfect IO, an early-stage, AI-driven platform that helps small businesses find and vet the right creators for their campaigns. Making such a system produce recommendations a business can actually rely on is a genuinely hard problem. “It means weighing every creator across dozens of signals, filtering out inflated or purchased audiences, and holding the whole thing to a standard where the output can be trusted,” he explains. Plugged directly into Meta’s and TikTok’s APIs, Pairfect turns what used to be days of manual digging into a vetted shortlist in seconds, giving small businesses a fast, low-risk way to make influencer marketing a genuine sales channel.
There is a neat logic to where Mazepin has landed. AI is the least predictable technology the industry has yet produced, and making unpredictable systems dependable is the one problem he has been solving, in one form or another, his entire career.
For all the changes of domain, the principle he keeps coming back to, in his code, his writing, and the advice he offers those following behind him, has never moved. “The technology keeps shifting, mobile, then finance, now AI,” he says. “What doesn’t shift is whether people can count on it. Make something people can genuinely rely on, and you’ve won their trust, and in this business, trust is the thing everything else is built on.”
