Recruiting for AI Roles — without sounding clueless.
Master the AI landscape, build credibility, and close higher-value roles.
Understand AI roles (ML Eng, MLOps, Applied AI, Data Science) and how they differ
Decode vague job descriptions into clear hiring intent
Ask recruiter-safe screening questions that build credibility
The Problem
Role titles are messy
Titles blur and tech stacks shift. Most recruiters misclassify roles, leading to weak candidate matches.
JDs are vague / buzzword-heavy
Clients say 'We need an AI engineer'. You'll learn to decode what they actually need vs. what they think they need.
Screening goes wrong
Asking weak questions destroys credibility. Over-indexing on buzzwords like 'LLM' costs you money and trust.
What You'll Learn
ML Engineer vs AI Engineer vs Data Scientist vs Applied Scientist: The clear map.
Research vs Product vs Platform AI roles: Where the talent lives.
Identifying when 'AI Engineer' is actually just a backend role with PyTorch.
Hiring intent signals: Fine-tuning vs Inference vs Pipelines.
Red flags in client requests: Spotting unrealistic expectations early.
Tech stack realism: What matters vs. what's just noise.
MLOps: The hidden bottleneck in AI hiring you need to understand.
Smart, recruiter-safe questions that don't require an engineering degree.
Spotting impostors vs researchers vs true builders.
Workshop Agenda
AI role map + archetypes
A definitive guide to the modern AI talent landscape.
Hiring-intent decoder
How to read between the lines of sloppy JD language.
Recruiter-safe screening questions + red flags
The toolkit for technical vetting without being a developer.
Q&A + live examples
Apply the framework to your actual open roles in real-time.
This is for you if:
- Generalist agency recruiters struggling with AI titles
- Tech-specialist recruiters wanting to master 'AI-first' hiring
- In-house TA at scale-ups & enterprises with high-stakes roles
- Team leads buying seats for their high-performance team
This is NOT for you if:
- Engineers looking to learn AI development or math
- Recruiters looking for deep ML theory with no practical application
- People wanting a generic tech recruitment overview
What's Included
Get Your Ticket
About Michal Juhas
I've spent 20+ years in IT, starting as a developer in 2004 and growing into a CTO. I've recruited for top companies such as Kiwi.com and Booking.com across the US, UK, and EU.