Financial technology (Fintech) changes incredibly fast, with AI and Blockchain pushing the limits each day. Keeping up demands solid technical roots and a sharp eye for what’s next. That’s the path Oleg Kubrak has taken. With over 14 years in software development, he started with core Java engineering and is now actively researching how AI and Blockchain could shape the next generation of Fintech solutions.
We spoke with Oleg, currently based in Kyiv, Ukraine, about how he made that shift, the projects he’s tackled, and where he sees things going.
Oleg, thanks for joining us. Your career spans over 14 years, starting in what many would call traditional software engineering. Can you tell us about those early days? What drew you to system programming and Java development?
Thank you for having me. My journey began with a fascination for how complex systems work. I pursued System Programming at Kyiv National Aviation University, which gave me a strong theoretical and practical foundation. Java, back then and still now, was a powerhouse for building robust, enterprise-level applications.
My initial focus was on backend development, particularly for systems needing high performance and reliability. It was about solving complex logical problems and building things that could handle significant load – challenges I still find incredibly engaging.
Even early on, improving how distributed teams collaborated was a key interest, driven by practical challenges. For instance, back in 2013, I was working at a Swiss financial institution where our development teams were spread across multiple countries — we had engineers in Ukraine, Latvia, and other locations. Everyone depended on a centralized SVN server based in Switzerland, and access to it was often unstable. Developers in different regions would regularly get blocked because the server was down or too slow. It was frustrating and clearly inefficient.
At that time, Git wasn’t widely adopted in enterprise environments, but I believed it was the right solution for its distributed nature. I prepared a detailed technical article, explaining exactly how and why we should migrate to Git, covering everything from practical steps to the architectural benefits of moving away from a single point of failure. It took effort, but eventually, I was able to convince senior management, and the entire development team — hundreds of people — made the switch.
That experience really solidified my thinking about system architecture. Although it wasn’t about blockchain, the core principle was similar: decentralization enhances resilience and efficiency. That mindset — eliminating single points of failure and empowering teams — is fundamental to how I approach modern infrastructure design today, especially in demanding fields like fintech and cybersecurity. It also showed the importance of technical leadership and the ability to influence architectural decisions across an organization
Your focus shifted significantly towards Fintech. What prompted that move, and what specific challenges in the financial sector captured your interest?
Fintech presented a unique convergence of challenges: The need for extreme reliability, massive scale (high-load systems), low latency, and iron-clad security. It demands real-time capabilities and resilience and it isn’t just coding tasks. The solutions are usually complex architectural puzzles requiring a deep understanding of distributed systems, data consistency, and performance optimization using technologies like Java, Scala, Kafka, and robust databases like PostgreSQL, often within cloud environments like AWS. The stakes are incredibly high in finance, and mastering that complexity is a huge motivator.
Then came the next evolution – incorporating AI and Blockchain. This seems like a significant leap from Java and distributed systems. What sparked this interest, and how did you bridge that gap?
It felt like a natural progression. While building and scaling these large financial systems, I saw recurring patterns and challenges where traditional approaches had limitations. Predicting market behaviors, automating complex operational workflows, enhancing security beyond conventional means – these were areas ripe for innovation.
I realized AI could offer powerful predictive capabilities and automation efficiencies, while Blockchain could provide unprecedented levels of transparency and security for transactions and data integrity.
Bridging the gap involved intense self-study, exploring online courses, reading academic papers, and, crucially, experimenting. It wasn’t about abandoning my core skills in Java and distributed systems, it was about augmenting them. Understanding how to integrate these new technologies effectively into existing, complex architectures became the key.
My background in building scalable, fault-tolerant systems was actually a huge advantage – you need that solid foundation to successfully deploy AI and Blockchain in demanding environments like Fintech.
AI is increasingly influencing the financial sector. Based on your technical background, where do you see the most meaningful opportunities for applying AI in Fintech?
Primarily, it is prediction and automation. For instance, using AI models to analyze patterns in transaction data to potentially flag anomalies or predict bottlenecks in processing flows before they happen. Another area is automating certain back-office operations, reducing manual effort and error rates.
Right now, I’m primarily focused on learning how to design AI-ready architectures — systems that can eventually support data-driven components in a secure and scalable way. That foundational work is essential before any real AI feature is implemented at scale, especially in the financial sector where risks are high.
The goal is always tangible business value – making systems smarter, faster, and more reliable. These areas are also subjects I’m currently exploring for potential future research and technical publications.
And Blockchain? It’s often hyped, but what are the practical applications you’re seeing in Fintech?
Beyond the hype, Blockchain offers concrete benefits, especially around security and data immutability. I’ve explored potential integration strategies for enhancing security using blockchain, particularly in the context of tamper-proof audit trails and data integrity.
It’s not necessarily about replacing existing systems wholesale but about strategically applying Blockchain where its core strengths – decentralization, transparency, security – provide a distinct advantage. At this point in my career, I’m beginning to formalize some of my ideas for publication in academic or professional journals.
This journey clearly involves continuous learning. How do you stay current with technologies that evolve so rapidly, especially while managing demanding projects?
It requires discipline and genuine passion. I dedicate time regularly to reading technical journals, following key researchers and developments, participating in online tech communities, and taking courses
Critically, I try to apply new concepts in practice, even on smaller experimental projects, to truly understand their nuances. The theoretical knowledge is vital, but hands-on experience is irreplaceable.
Looking ahead, what are your main goals? What excites you most about the future of AI and Blockchain in Fintech?
Oleg Kubrak: My primary goal is to continue developing my international career, focusing on architecting innovative solutions at the intersection of Fintech, AI, and Blockchain. I’m particularly interested in further exploring AI for sophisticated risk management and developing more autonomous, self-optimizing financial systems.
What excites me most is the potential for these technologies to fundamentally reshape finance – making it more efficient, accessible, secure, and intelligent. We’re still scratching the surface, especially in how AI and Blockchain can synergize.
Finally, what advice would you give to a software engineer, perhaps skilled in Java like you were, who is looking to transition into these emerging tech fields within Fintech?
First, build a rock-solid foundation in software engineering principles – distributed systems, data structures, algorithms, security. This core knowledge is transferable and essential. Second, be deeply curious.
Don’t just learn the ‘what’ but the ‘why’ and ‘how’ of new technologies like AI and Blockchain. Third, start small. Integrate a small AI feature or explore a specific Blockchain application relevant to your current work. Practical application cements learning. And finally, focus on solving real business problems. Technology is a tool, its value lies in the solutions it enables for the financial industry.
Oleg, thank you for sharing your fascinating journey and insights into the future of Fintech.
My pleasure. Thank you.
Oleg Kubrak is a Senior Software Engineer with over 14 years of experience in building enterprise-grade financial systems. Specializing in distributed computing, high-load architectures, and financial technology, he is currently expanding his expertise into artificial intelligence and blockchain applications within the fintech sector. He actively researches how emerging technologies can enhance automation, security, and performance in complex financial environments, with plans to share his insights through future publications and technical contributions. You can connect with him on LinkedIn.
