Overview
MLOps & AI Infrastructure is where the rubber meets the road. These teams own the systems, platforms, and infrastructure that operationalise machine learning — from experiment tracking and model deployment through to monitoring, governance, and scaling. Without MLOps engineers, AI stays in notebooks. With them, AI becomes a production business capability.
Roles We Place
- MLOps Engineers
- ML Platform Engineers
- Cloud Engineers
- DevOps Engineers (ML)
- Site Reliability Engineers (AI)
- Infrastructure Engineers
Tech Stack
Kubeflow, SageMaker, Vertex AI, MLflow, Kubernetes, Docker, Terraform, Helm, ArgoCD, Prometheus, Grafana, AWS/Azure/GCP
Typical Hiring Scenarios
Building an ML platform team to support 50+ data scientists — Your data science team is growing fast, but they're drowning in infrastructure toil. You need MLOps Engineers and ML Platform Engineers who can standardize experiment tracking, model deployment, and infrastructure provisioning so scientists can focus on modeling.
Hiring MLOps engineers to reduce model deployment time from weeks to hours — Your release cycle is a bottleneck. You need MLOps Engineers who can design CI/CD pipelines, automate model validation, implement continuous monitoring, and reduce deployment friction with tools like Kubernetes, ArgoCD, and custom MLflow integrations.
Scaling cloud infrastructure for a generative AI product handling millions of requests — You're seeing explosive demand for your AI product. You need Cloud Engineers and SREs who understand cost optimization, auto-scaling, multi-region deployments, and can keep your infrastructure stable as traffic scales.