I source MLOps, ML Platform, and AI Infrastructure engineers from their actual GitHub commit history, Hugging Face deployments, and practitioner communities — not just LinkedIn. Verified technical depth. Practitioners your pipeline hasn't reached.
The methodology
"I find MLOps engineers from their actual GitHub commit history, Hugging Face model deployments, and practitioner community forums — not just LinkedIn."
Why it matters
MLOps engineers receive 15+ generic recruiter messages per week.
I distinguish engineers who own production pipelines from those who contributed three lines to a popular fork. Technical depth verified from their actual public work — before I ever reach out.
The result: candidates who are qualified, who haven't been burned by a dozen other approaches, and who respond because the outreach is specific to their actual work.
How I source
Four layers. One verified brief.
Searches across public repositories by actual code committed. Identifies engineers who own production ML pipelines — not just forks.
Model deployment history and community standing. Surfaces practitioners with real production experience before they appear on recruiter radar.
MLOps Discord servers, practitioner forums, and specialist communities. Engineers solving hard problems publicly — reached before competitors.
Every candidate delivered with verified skills, production evidence, compensation context, and a tailored outreach angle. Not a list of names.
Specialist focus
Built for one specific problem.
Narrow specialisation means deeper candidate networks, faster searches, and outreach that actually converts.
Work together
Send me the job description. I'll run a free sample search and you compare my output directly against what your current sourcing produces. No commitment, no pitch deck — just results.