Inside the AI Engineer Role: Tools, Skills, and Career Path
Details
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
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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