We went through current roles across AI, GTM, RevOps, sales engineering and automation to understand what companies are actually asking for — and which skills sit right at the intersection.
Live · 75 minutes · with Q&A at the end
Real names of roles currently open. See the full map →
Prompt engineering, machine learning, agents, Python, n8n, Clay, HubSpot, RAG, APIs, RevOps, GTM engineering. It all sounds important. None of it tells you where to enter.
"What exactly should I learn?"
"Do I have to become a machine-learning engineer?"
"Will prompt engineering still be worth anything?"
"What jobs actually exist — not in a Twitter thread?"
"How do I become useful enough that someone pays me?"
"How do I get experience if nobody gives me experience first?"
Researching models is one layer. Building them is another. Connecting AI to systems, workflows and business outcomes is a third — and it's the one showing up again and again in current job posts. We'll read the real posts and see what they ask for.
Breadth across business, revenue, AI, automation, data and systems — with real depth in one or two. Nobody has all five layers. We'll look at where you already stand and what comes next, depending on whether you come from marketing, sales, operations or development.
Nobody hands you the first job so you can learn. But you can build small real systems and document them the way a professional would: problem, inputs, architecture, human checkpoints, metric, result, what failed and what you changed. That's a portfolio. "I know ChatGPT" isn't.
The structure of a portfolio case that reads like professional work.
Four different routes toward the same intersection. In the session we walk through each one with what to learn first.
PTS AI builds and operates AI-assisted revenue systems for multi-branch companies. Which means: we're exactly the kind of company that posts these roles — and screens for them. What we teach is what we look for when hiring, not a theory about the future of work.
You come from marketing, sales, RevOps, operations, development, freelancing or consulting — or you're switching careers.
You're interested in AI applied to real work, not AI as a conversation topic.
You're willing to build small things and document them while you learn.
You'd rather see market evidence than income promises.
You want a guaranteed job or a promised salary. We promise neither.
You want fast income without building anything.
You're preparing for ML research or a PhD. That's a different layer.
You expect a list of prompts and nothing else.
No cost. We send you the link, and the skill map lands in your inbox after the session.
The link is on its way by email. If you want to arrive ahead: pick one repetitive process in any business you know, and write in a sentence what should happen automatically and where a person would still have to decide.