MLOps Engineering Recruitment

Hiring MLOps engineers isn’t about finding someone who “knows ML” — it’s about finding people who can turn research into reliable, scalable, production-grade systems. We specialise in identifying engineers who understand the full lifecycle: data, training, deployment, monitoring, and continuous improvement.

We look for engineers who can:

• Build and maintain ML pipelines and CI/CD workflows
• Deploy and manage models using a mix of open-source tooling (e.g. MLflow, Kubeflow) and managed platforms (e.g. SageMaker, Vertex AI) 
• Manage containerised workloads (Docker, Kubernetes)
• Implement monitoring, observability, and model governance
• Optimise inference performance and cost

We know the difference between someone who has usedthese tools and someone who has built with them.

Our network includes MLOps specialists who have shipped real systems — not just notebooks. We understand the nuances of production ML, so our shortlists reflect genuine capability, not buzzwords.