|
Hi {{ name }}, “I am in confusion, some people say ML is required for AI engineering.” I’ve received some version of that message more times than I can count this year. So here’s the short answer, side by side. |
|
| ML Engineer | AI Engineer | | Main job | Trains and tunes models | Builds products on top of existing models | | Daily tools | PyTorch, GPUs, training pipelines | Python, LLM APIs, RAG, agents, LangGraph | | The hard part | Getting a model to learn | Making a system accurate, safe and affordable in production | | Needs first | Strong maths and statistics | Solid Python and engineering habits |
|
Many job posts still say “AI/ML” because they were copied from older templates. Read the responsibilities and ignore the title. If the work is LLMs, RAG, agents and evaluation, it’s an AI Engineering role, and you won’t be quizzed on backpropagation. |
|
// NOTE
A 30-second test for any job description
Count how many of these appear: LLM, RAG, agents, LangChain, LangGraph, vector database, prompt engineering, evaluation.
Three or more? That’s AI Engineering. Mostly PyTorch, model training and feature engineering? That’s ML, and a different path.
|
|
AI Engineering is where most of the new roles are being created right now. It’s open to anyone who can write good Python and think like an engineer, whether you’re in college or 15 years into your career. If you want to see the full path, from your first line of Python to a deployed multi-agent system, I walk through it in this video. |
|
BC |
Balaji Chippada
AGENTIC AI ENGINEER & EDUCATOR · 9+ YRS PRODUCTION AI |
P.S. Next email: the five things AI Engineers actually build at work, with real examples. | |
Build it. Don’t just watch it.
|
|