Inside the AI Engineer Role: Tools, Skills, and Career Path
The AI engineer role is emerging quickly, but it's still unclear how it differs from data science or ML engineering, what the daily work looks like, and which skills matter.
In this podcast, we'll talk with Ruslan Shchuchkin, a Gen AI Engineer at Finanzguru, about his path from customer success to data science and, eventually, to building production systems with generative AI.
We'll explore how engineers transition into this role, what kinds of projects help break into the field, and which tools are commonly used when developing LLM-based 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
Why personal projects help engineers transition into AI roles
Like many engineers transitioning into AI engineering today, Ruslan's path included building personal projects, learning new tools, and navigating career challenges, such as layoffs and technical interview preparation.
About the Speaker
Ruslan Shchuchkin is a Gen AI Engineer at Finanzguru, where he builds production systems using LLMs and generative AI tools.
He began while working as a Customer Success Manager, when he saw a Tableau dashboard that sparked his interest in data. That moment led him to pursue a data-focused master’s program, where he began working on machine learning and NLP projects.
He later worked as a data scientist at OLX Group and Smart Steel Technologies, building experimentation frameworks, recommendation systems, and NLP pipelines.
Over time, his work evolved toward generative AI and LLM-based systems, which he sees as a natural continuation of his work in natural language processing.
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