Deploying and Optimizing LLM Inference in Production on GPUs Ranging from L4 to 4xH200: Choosing Inference Engines and Configurations, Balancing Throughput, Latency, and Cost, and How to “Squeeze” the Most Out of Your Hardware and Avoid Common Tuning Mistakes

Over the past few months we have been building one of the subdomains of our lending project, with most of the code written by AI under architectural supervision: 100 stories in 26 days, 12 repositories, ≈218,500 lines of code, 44 ADRs, including PCI DSS and National Bank of Ukraine requirements, where the cost of an architectural mistake is quite high. This experience changes the way we look at the architect's work when planning project decisions. Although AI tools help automate and accelerate development, in fact it is the human who remains responsible for the result. By default, AI does not think strategically and often picks the simplest implementation path, which can lead to negative outcomes in the future unless a human explicitly describes the strategy when setting the task. So the more autonomy we want from AI, the higher the qualification required of the human beside it: there remain decisions that cannot be delegated, no matter how smart the next model gets. Modern methodologies (ADR, quality gates, ATAM, TOGAF) are transforming in the age of AI and make it possible to achieve a higher-quality result with higher autonomy. That is what we will talk about in my presentation. This talk is for architects, team leads, and senior engineers who have already tried AI and strive to increase the effectiveness and autonomy of their teams.

How would you call a tech lead who reads and checks every single line of code their team writes? For me it is not a rhetorical question: in my career I built the contribution process for one of the largest repositories at Grammarly, for a hundred contributors monthly. Today every one of us has become a tech lead of a team of AI agents, and we ask ourselves and each other the same question: how do you call a person who does not read the code their agents generate? Without going into the full controversy of that question, I believe the answer is fundamentally the same for a team of people and a team of agents. Properly built processes let the lead avoid micromanagement and reviewing every line in a pull request. In this talk I will show pragmatic recipes for building AI development that will bring you closer to trusting the code your AI agents write.

Not everyone supports the new strategy. The AI initiative is being sabotaged. The architect is asking for two more quarters. Marketing wants to launch as early as next week. Sound familiar? The problem is that we often expect other people to follow the same logic we do. But in the product business, different “species” work side by side: those who champion people; those who champion experiments; those who champion stability; those who champion results. And until we learn to see these differences, any strategy risks remaining nothing more than a pretty slide. Using examples from product teams and AI transformations, we’ll explore how the framework of competing values works and how to use it for influence, negotiation, and change management. 📌 Key takeaways: Every stakeholder defends a value that is rational for them. “Everyone is right—but only partially.” How to read motives behind words and objections. Why the strongest products know how to switch between languages of influence. How to reduce resistance to change without pressure. How to build support around product decisions.

In technology companies, AI is already going beyond the trend and influencing products, engineering, and daily business processes. Along with opportunities come challenges: working with sensitive data, security requirements, and the complexity of integration into existing systems. In such conditions, it is important to understand where AI really creates value and where it just adds noise. During the discussion, participants share their practical experience: how they implement AI in their products, adapt engineering processes, and restructure operations for new tools. This is a conversation about real pain points, opportunities, and solutions that help teams move forward.

As the team grows, products multiply, and the engineering organization evolves - the CTO role often shifts from a technical leader to a manager staring at spreadsheets and dashboards. And that’s when it becomes truly challenging: to stay in the technical loop, remain close to the architecture and systems, and keep a real sense of what’s happening on the ground. In this talk, I’ll share my experience on how to stay a Hands-on CTO - maintaining deep technical understanding, influencing architecture, and scaling the company at the same time, without losing touch with the code and core systems. Key topics we’ll cover: - How the CTO role evolves as the company grows - from coder to system-level leader. - The most common traps: loss of architectural memory, disconnect between business and engineering, dependency on a few key people. - Healthy habits that help keep technical context: code reviews, architecture stand-ups, "walk the code" sessions, CTO R&D sprints. - Decision-making systems: how ADRs and RFCs help maintain clarity and traceability. - Communication with tech leads: building effective 1:1s, technical syncs, and knowledge-sharing loops. - Tools and platforms that preserve context - dashboards, monitoring systems, and architecture reviews.

Being a team lead is like piloting a spaceship where every crew member has their own temperament and trajectory. The DISC model is your GPS that helps you avoid burning up in the atmosphere of “storming” and guides your team into the stable orbit of “performing.”

In his presentation, Vadym will cover: - Goals of the strategy process - Forecasting and goal setting (Foresight, Top-Down + Bottom-Up) - Lean A3: structuring problems and metrics - Strategy implementation and cascading goals - Tracking results (time cycles, OKRs, monitoring, adaptation) - Practical A3 examples

Real experience of building a team from 1 to 30 people: challenges, solutions, and achieved results. We will discuss the team selection strategy, key tasks, implemented tools, as well as recommendations on how to stay afloat in this challenging industry.

How do you transition from a developer to a successful entrepreneur and build a multimillion-dollar business? The real-life journey of founding an AI company in the U.S., raising $2.5M in investments, and key insights into growing a tech business.