Berlin DataTalksClub Group

​Ruslan Shchuchkin is a Gen AI Engineer at Finanzguru, where he builds production systems around large language models (LLMs) and generative AI (GenAI) tools.

​Before moving into AI engineering, Ruslan worked as a data scientist at Smart Steel Technologies and OLX Group. At OLX, he focused on experimentation, recommendation systems, NLP pipelines, and model evaluation frameworks. His work included building data-driven personalization features, designing ML training and evaluation workflows, and applying techniques such as clustering, topic modeling, and named entity recognition on marketplace data.

​Over the past few years, his career has followed a path that many engineers are now exploring: moving from data science and ML experimentation toward building full AI-powered systems and applications.

​In this conversation, we will discuss:

• ​How the AI engineer role differs from the data scientist and ML engineer roles
• ​What tools and technologies are used in production AI systems today
• ​How experimentation, evaluation, and deployment change when working with LLMs
• ​What skills aspiring AI engineers should focus on building today

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​About the Speaker:
​Ruslan Shchuchkin is a Gen AI Engineer at Finanzguru. He has a diverse background spanning Customer Success and Data Science, having previously built scalable machine learning frameworks at OLX Group. Having navigated the mental health challenges of the "Data Science roadmap," Ruslan is a strong advocate for biohacking and mindfulness within the tech community.

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Even though the Data Engineer title has been around for a long time, it can mean different things across companies.

​In this episode, we’ll explore the reality of the Data Engineer role in 2026 with Slawomir Tulski, a Data Engineer who spent much of his career at Meta, where he moved between individual contributor and manager roles and conducted hundreds of technical interviews while helping shape the company’s Data Engineering hiring process.

​Today, Slawomir works as an independent consultant, advising companies on structuring data teams and maximizing value from their Data Engineering efforts. His recent ebook examines the current Data Engineering landscape and why the role is often misunderstood across the industry.

​In this conversation, we will discuss:

• ​Why does the term Data Engineer still create confusion across the industry
• ​The different specializations inside Data Engineering and how they differ in practice
• ​How the same role changes depending on company maturity and team structure
• ​What companies look for when hiring Data Engineers
• ​Common career traps and misleading job descriptions that engineers should watch for
• ​How the role may evolve as AI transforms the data ecosystem​

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About the Speaker:

​Slawomir Tulski is a Data Engineer who spent the bulk of his career at Meta, moving between Individual Contributor and Manager roles. During his time there, he focused on scaling the Data Engineering support for the Meta Ads ranking system. Beyond his direct team, he was also heavily involved in global hiring, conducting hundreds of interviews and helping shape the company's data engineering recruitment process.

​Today, he works as an independent consultant, helping businesses get more out of their Data Engineering teams. On the personal side, he’s a dad, a husband, and a failed bass player.

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