As a child, like many others, I was asked: “What do you want to be when you grow up?” I answered: “I want to do something with computers.” The answer was not especially insightful, yet, as it turned out, remarkably accurate. I did not yet know that this “something” would stretch across my entire professional life — from systems code and databases to enterprise platforms and artificial intelligence.

I went to study at MIEM, in the program “Computing Machines, Complexes, Systems and Networks.” Formally, we were being prepared to design computers. In practice, it meant immersion in how computing systems work — deep enough for the computer to stop feeling like a magic box and become an engineering object.

Even my thesis turned out to be a harbinger of the future: I explored the use of neural networks in printed circuit board design. At the time, artificial intelligence was not on every television screen, and neural networks could neither hold a conversation nor draw cats. It was a fairly specialized engineering topic. I encountered it, completed my thesis, and moved on for a while. Many years later, it turned out that some topics know how to wait patiently.

My professional path began with systems software development: C++, ObjectStore, Microsoft SQL Server, and other tools available at the time. Fairly quickly, code led me to data management systems — first to their development, then to architecture and implementation. Products, scales, and discipline names changed; what had once been databases and warehouses gradually became data platforms. But the thread itself never broke: almost my entire subsequent career was, in one way or another, connected to how data is stored, linked, protected, turned into knowledge, and used within organizations.

In my third year, there was an encounter to which I may have attached more significance than it deserved — but I still made a note to myself. I met a former classmate who had been expelled for poor academic performance. While I was diligently working toward my degree, he looked great, earned well, and worked at PricewaterhouseCoopers. The picture was slightly unsettling: someone without a degree was already where someone with a future degree was only planning to go.

From that encounter I drew a practical conclusion: my knowledge and engineering way of thinking would probably be best valued by a large international technology company — with serious challenges, resources, and a culture of professional development. The first steps did lead in that direction: international CNET, then programs and implementations around SAP. More than ten years later, I joined IBM. By then IBM had acquired PwC’s consulting business — and the two threads unexpectedly crossed. Not quite by the route one could have imagined in the third year, but careers rarely respect pre-drawn maps.

Working in international companies became a school of its own for me. Corporate culture showed itself not in slogans on the walls, but in small things. In a Swiss company, all business correspondence was conducted only in English. In an American one, languages could freely switch within a single email thread. Behind such details lay more important habits: recording agreements, delivering on commitments, discussing problems calmly, and carrying a solution through to the client — not just to the next presentation.

The years at IBM were especially important. I worked across a wide range of technologies and projects, but the main thread remained Information Management, later folded into Data & AI. Development, architecture, enterprise warehouses, information management, analytics solutions, implementations, and Design Thinking sessions — the tasks changed, but behind them stood one question: how to make complex technology work inside an even more complex organization.

Scale changed too. Instead of a single module — an implementation program. Instead of one team — distributed specialists from different countries and divisions. Instead of a technical result alone — timelines, economics, risks, client expectations, and the life of the solution afterward. Banks, telecom, large enterprises: in such projects, technology is tested not only by architecture, but by reality.

Later, work at Russian vendors gave a different experience. Here it was required not only to run projects, but to create the environment in which they could exist at all: building a consulting practice almost from scratch, establishing processes, forming teams, and linking product, engineering, and commercial agendas. International companies taught me the discipline of complex delivery. Russian ones — to act where part of the structure has to be assembled while in motion.

I felt differences in corporate cultures especially clearly. People with experience in international companies could often be recognized not by English words in their speech, but by work habits: how they prepare a solution, run a meeting, record accountability, and treat commitments. Russian corporate culture continues to take shape, sometimes quickly and unevenly. Yet it has its own strengths — speed, ingenuity, and the ability to act with incomplete inputs. I had the chance to live inside both systems, see their merits and limitations, and learn to translate from one organizational language to the other.

Today I describe myself as an expert and technical leader at the intersection of data, artificial intelligence, enterprise platforms, and complex implementations. It is not easy for me to fit my experience into a single job title. Over the years I have led development, implementation programs, consulting, and product and engineering work. Depending on scale and task, this can look like the role of CTO, Head of Data & AI, consulting director, or leader of a complex program. But the through-line is one: connect technology, people, and organization — and carry an idea through to a working solution.

Artificial intelligence has now become the main field for this. The neural networks from my thesis returned years later — no longer as an academic topic, but as a technology that can be embedded in real processes. And here it is especially easy to mistake a flashy demonstration for a finished product.

For me, AI is not a showcase or a “department of wonders,” but another engineering layer on top of data, infrastructure, and organizational rules. You need to find a use case, test it on live material, align with IT and information security, launch a pilot, see the limits, and help the solution take root. Sometimes a hypothesis should be developed. Sometimes — changed. Sometimes it is more useful to acknowledge in time that it did not survive scrutiny. The last is usually harder than preparing another presentation.

On this site, two scales of my work come together. My primary professional experience is enterprise programs, large teams, complex clients, and multi-year implementations. In the cases section, that experience sits alongside a compact applied lab. In enterprise programs, scale, delivery, and the ability to unite teams and organizations matter. In lab projects, I can set the task myself, choose the architecture, organize the work, and when needed go deep into the code. The full path from hypothesis to a working solution is visible here in its entirety.

These experiments let me not talk about new technologies from the sidelines, but test them with my own hands: where they are genuinely useful, where they require mature data and processes, and where they remain a beautiful demonstration for now. They do not replace the story of large corporate programs — they continue it in a different format and show the direction I am moving in today.

That is why the cases section is not a collection of technologies for their own sake. It includes programs to build data platforms and develop client capabilities, alongside an enterprise knowledge base and AI assistant, a research ML environment, knowledge extraction from Telegram, and secure work with cloud models. Scale and format differ, but the principle is one: technology gains meaning when it starts working for people and organizations.

If this approach resonates with you — see the cases or get in touch directly.