I mapped every AI role hiring right now — here's what they actually want
I've spent the last 6 months talking to AI hiring managers at startups and big tech. Here's what nobody on Twitter is telling you:
The roles that are actually hiring (and what they pay):
🔹 Prompt Engineer — $80-150K. Lowest barrier to entry. Companies need people who can systematically get better outputs from LLMs. You don't need to code, but it helps.
🔹 ML Engineer — $130-220K. The workhorse role. You take research models and make them work in production. Python + MLOps + system design.
🔹 AI Product Manager — $140-200K. You don't build the models, you decide what to build and why. Requires strong business sense + enough technical knowledge to talk to engineers.
🔹 RAG/AI Application Developer — $120-200K. The hottest role right now. Companies have data, they need AI that can actually use it. If you can build RAG pipelines, you're in demand.
🔹 Data Scientist — $110-180K. Still hiring, but the role is evolving. Pure analytics is getting automated. The winners combine stats with ML engineering.
What most candidates get wrong:
They study theory for 6 months instead of building
Their GitHub is full of tutorial clones, not original work
They can't explain their projects to a non-technical person
They skip system design prep (this is where senior roles are won/lost)
The fastest path I've seen work:
Week 1-2: Learn Python + core ML concepts
Week 3-4: Build 2 real projects (not tutorials)
Week 5-8: Deploy something live + build your portfolio
Week 9-12: Apply aggressively, prep interviews, get offers
That's the exact structure behind what I teach. If you want the full breakdown with hands-on projects, code, and a community of people doing the same thing — check out the AI Career Mastery Bootcamp.
What role are you targeting? Drop it below 👇