AI Hiring
MLOps / AI Infrastructure Engineer
Hiring the engineers who keep AI running: Kubernetes, Terraform, CI/CD, observability — the infrastructure layer that turns models into reliable systems.
Client
Companies scaling AI/ML infrastructure (semi-anonymous track record)
Challenge
AI infrastructure roles sit between DevOps and ML — and candidates strong in one side routinely fail the other. A wrong hire here silently degrades every model the company runs.
Hiring Strategy
We screen for evidence of running ML workloads specifically: GPU scheduling, model serving, pipeline reliability, monitoring of both systems and model quality — not generic cloud administration.
Search Process
Technical interviews built around production incidents: what failed, how it was detected, what was rebuilt afterwards. Infrastructure experience is verified through consequences, not certifications.
Result
A dedicated evaluation track for DevOps/MLOps/Infrastructure roles — one of the agency's standing specializations.
Technologies
- Kubernetes
- Docker
- Terraform
- GitLab CI
- Prometheus
- Grafana
Business Impact
Clients get infrastructure engineers who understand ML workloads — the difference between AI that runs and AI that quietly breaks at 3 a.m.
Key Takeaways
- Incident stories are the most reliable infrastructure interview material.
- ML workloads have failure modes generic DevOps never sees — screen for them explicitly.
- Monitoring model quality is as much an infrastructure job as monitoring servers.
Don't lose months and budget on the wrong AI hire.
A 30-minute breakdown of your challenge. No pitch. We'll show how we'd approach it ourselves — including the option not to hire yet.