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: 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

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The AI Skills Software Engineers Need to Learn Now is an episode from Beyond Coding by Patrick Akil.

This episode is 00:44:22 long.

This episode was published on Jan 7, 2026.

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