The AI Skills Software Engineers Need to Learn Now

Software engineers often think adding AI is just a simple API call, but moving from a Proof of Concept to a stable production system requires a completely different mindset.

Maria Vechtomova breaks down the harsh reality of MLOps, why rigorous evaluation is non-negotiable, and why autonomous agents are riskier than you think.

In this episode, we cover:

• The essential MLOps principles every software engineer must learn
• How to bridge the gap between a demo and a production-grade solution
• Strategies for evaluating agents and detecting model drift
• The security risks of customer service agents and prompt injection
• Practical tips for using AI tools to boost your own productivity
• 
  

Connect with Maria:

https://www.linkedin.com/in/maria-vechtomova

Timestamps:

00:00:00 - Intro

00:01:25 - Why the AI Hype Was Actually Good for Monitoring

00:03:07 - Real-World AI Use Cases That Deliver Actual Value

00:05:16 - MLOps Basics Every Software Engineer Needs to Know

00:08:08 - The Hidden Complexity of Deploying Agents to Production

00:12:02 - Minimum Requirements for Moving from PoC to Production

00:15:41 - Step-by-Step Guide to Evaluating AI Features Before Launch

00:18:08 - How to Handle Data Labeling and Drift Detection

00:21:55 - Why You Likely Need Custom Tools for Monitoring

00:24:56 - Why Engineers Build AI Features They Don't Need

00:26:01 - How Software Engineers Can Learn Data Science Principles

00:31:36 - The Dangerous Security Risks of Autonomous Customer Service Agents

00:34:44 - Why Human-in-the-Loop is Essential for Avoiding Reputational Damage

00:36:18 - Boosting Developer Productivity with Opinionated AI Prompts

00:39:20 - Using Voice Notes and AI to Organize Your Life

#MLOps #SoftwareEngineering #ArtificialIntelligence