AI Engineering for DevOps, MLOps and Software Engineers

Are you using AI like a search engine or like a senior engineering partner? In this video, we break down practical, real-world AI workflows from the book "50 AI Workflows for Engineers: From Debugging to System Design, Code Review & Engineering Automation". Designed specifically for Software, DevOps, and MLOps engineers, this guide shows you how to stop using AI casually and start using it systematically to automate the mechanical parts of your job and save hours every week . 📚 GET THE BOOK ON AMAZON: Level up your engineering career and master AI workflows today: https://www.amazon.com/Workflows-Engineers-Debugging-Engineering-Automation/dp/B0GZJNMY9C What you’ll learn inside the book: • Sprint Planning: How to turn vague Jira tickets into clear implementation plans in just 15 minutes • Troubleshooting: A systematic debugging workflow that cuts your diagnosis time drastically • AI Quality: How to build LLM-as-Judge automated evaluation pipelines to reliably measure AI output at scale • Advanced Leverage: The multi-model strategy that top engineers use to get the most out of ChatGPT, Claude, and Gemini • Automation: How to build AI agents with tool use, the Model Context Protocol (MCP), and multi-agent orchestration • Ready-to-Use Systems: 50 battle-tested workflows and prompts drawn from real production systems that you can copy and use immediately Every chapter includes a real engineering story from production systems at scale, a step-by-step workflow with copy-paste prompts, a breakdown of common failure modes to avoid, and a quick reference card so you can use the workflow without re-reading the chapter About the Author: Arian Hosseini is an ML Tech Lead with over 60 papers and patents, an ACM Test-of-Time Award, and years of experience building production AI systems at companies like Amazon, Microsoft, Comcast, and Samsung.