How to Build AI that actually Ships in Production
Aleksandr Kim has spent nearly a decade doing what the industry now calls AI engineering, long before the title existed. As a Senior Data Scientist at Intuit in London, he builds AI-powered features in production at scale, and his career traces the full arc from data scientist to ML engineer and now AI engineer, where, as he puts it, the labels keep changing but the actual work barely has.
In this conversation, Aleksandr unpacks one hard-won lesson from the front lines of enterprise AI: the model is rarely the win. He walks through the agentic system he architected at Intuit, one that aggregates data, auto-generates reports, and delivers them straight to leadership in Slack, saving over 30 hours of executive time a week, and explains why it only worked after he killed the original chatbot idea.
What we get into:
Why AI didn't shrink the gap between proof-of-concept and product. It just made the PoCs cheaper, and the unglamorous work between demo and deployment is still very much there.
The agentic system that pivoted from chatbot to automation, and why customer interviews, not better modeling, were what made it ship.
Translating ML metrics into business outcomes, turning precision and recall into things like First Contact Resolution and automation rate, on day one.
Cost-efficient AI at scale, using cheap models for the easy cases and saving the LLM judge for the hard ones, cutting inference spend by about a third.
Knowing when to abandon, the underrated senior skill of recognizing a problem that simply won't move, no matter the infrastructure or team.
If you're an engineer or data scientist trying to figure out where your value sits in the LLM era, especially inside a large company, this one's for you.
About the speaker:
Senior Data Scientist at Intuit, based in London — doing what most people outside the company would now call AI engineering: building AI-powered features in production at scale. About 9 years across banking (Raiffeisen), cybersecurity (Kaspersky), retail (X5 Retail Group), and fintech (Intuit), with experience on both sides of the IC–management line: I led two data science teams at X5 Retail Group before moving back to IC at Intuit, and I founded and run Intuit's 70+ member Data Science Guild. Inventor on 15+ ML/AI patents.
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