Engineering the Foundations of AI: A Conversation with Danil Temnikov

In the rapidly evolving world of AI, it's easy to focus solely on the models—their size, performance, or the headlines they generate.

But behind every successful AI deployment lies a robust data infrastructure foundation—Danil Temnikov understands this better than most.

With a career rooted in backend systems, cloud engineering, and data access architecture, Danil quietly powered systems that make AI possible, scalable, and sustainable.

Now a Lead Software Engineer at EPAM Systems, he specializes in integrating generative AI into business environments—not by building models from scratch but by enabling companies to use off-the-shelf AI effectively and responsibly.

We sat down with Danil to unpack what modern AI infrastructure looks like, how engineers can stay relevant, and why software engineering is becoming more about orchestration than construction.

Danil, your journey into engineering is anything but typical. How did it begin?

Danil: I was lucky to have early access to the internet, which was rare in my small Siberian town. By the time I was in middle school, I was already creating websites—first with HTML, then with early no-code tools that let me build chat rooms and forums. That curiosity led me to programming competitions and eventually to writing automation scripts, even while working in PR and marketing.

It took me some time to realize that my real drive was solving problems with technology, and I fully transitioned into engineering.

You didn't go straight into tech. Does your background in PR and marketing influence how you work as an engineer today?

Danil: Absolutely. Understanding user needs, thinking from a business perspective, and communicating clearly are underrated engineering skills.

When I build infrastructure today, I'm not just thinking about code efficiency. I'm also considering how someone will use this system, how it fits into a business flow, and how to make it easy for teams to adopt and scale.

You're now leading infrastructure for a major financial software company via EPAM. What does your role involve?

Danil: Most of my day-to-day work revolves around data enablement. That includes designing systems that allow different teams—analysts, data scientists, and business users—to access and work with data securely, reliably, and at scale.

It's not just about building pipelines. It's about managing data governance, optimizing storage and computing, and ensuring that the systems we build support everything from BI dashboards to machine learning workflows. My job is to make sure the plumbing works so that when a team wants to use AI, they're not waiting three weeks to get the data.

You also work with GenAI but not as a model developer. What's your perspective on generative AI in enterprise settings?

Danil: I focus on integrating GenAI into real-world business workflows. That means helping teams use tools like GPT-based APIs effectively to automate report generation, enhance internal search systems, or accelerate document processing.

The challenge isn't the technology itself. It's figuring out where it fits, how to govern its use, and how to make it deliver real value without introducing unnecessary risk.

In many cases, off-the-shelf GenAI models are good enough—you need solid infrastructure and smart design around them.

Let's talk about scalability. What do most teams get wrong when trying to scale AI or GenAI?

Danil: They focus on the model and forget the environment it runs in. You can have the best AI engine in the world, but if your data systems are slow, fragmented, or insecure, you'll never get value from it.

Scalability isn't just about hardware. It's about architecture. Can your system handle concurrent requests? Can it control access without adding friction? Can it adapt as new data comes in or policies change? Those are infrastructure questions, not just ML ones.

You've worked across Russian and US engineering cultures. What was the biggest adjustment for you?

Danil: In Russia, engineering is often more siloed. You're given a spec, and your job is to build exactly what's described. In the US, especially in product-driven environments, engineers are expected to engage earlier—ask why, challenge assumptions, and contribute to design.

That shift was hard at first. But now, I enjoy being part of the entire lifecycle—discovery, planning, building, iterating. It's more collaborative, and ultimately, it leads to better outcomes.

What's the most rewarding project you've led recently?

Danil: One standout was building Russia's first online brokerage platform for legal entities. There was no blueprint—no one had done it before at that scale. We had to design everything from scratch, balancing compliance with usability and performance. It was a crash course in both regulation and innovation.

Another project I'm proud of was a multi-cloud integration pilot, enabling seamless data transfer between AWS and GCP with minimized egress costs. It was an infrastructure challenge with real business stakes.

Where do you see the role of software engineers heading in the age of AI?

Danil: I don't think AI will replace programmers, but it will change what we do. The future engineers won't just integrate APIs—they'll orchestrate multiple AI agents, managing context, flow, and reliability.

Understanding models, yes—but also understanding infrastructure, governance, and how systems talk to each other. The complexity is increasing, not decreasing. Engineers who can connect the dots will be the most valuable.

And what about regulation? Do we need stricter rules for AI?

Danil: Regulation is essential, but it needs to be smart. Too much, too early, and we risk stalling innovation. Too little, and we face actual harm.

The best path is collaboration—between engineers, researchers, and policymakers. We must build adaptable, context-aware frameworks and be informed by real-world usage.

What would it be if you could give one piece of advice to engineers building infrastructure for AI?

Danil: Chase reliability, not hype. Your job is to make things work consistently, not just impress on demo day. Focus on the fundamentals—clean architecture, observability, access control, and scalability.

And never stop learning. AI is moving fast, but so are cloud tools, data systems, and best practices. The engineers who stay curious and adaptable will succeed.

Wrap-Up

AI continues to evolve, and the spotlight turns to the infrastructure and engineering that make it usable, scalable, and impactful. Engineers like Danil shape the foundation of tomorrow's intelligent systems, bridging the gap between raw data and real-world AI integration.

Because, for him, real innovation happens behind the scenes, where infrastructure meets insight.

About the Author

Lea Smith is a technology journalist and content strategist who is passionate about making complex topics accessible. With a background in digital media and AI, she writes about the intersection of innovation, ethics, and engineering. Her work focuses on telling the human stories behind tech breakthroughs and highlighting the experts shaping our digital future.