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Infrastructure specialists who build and maintain ML pipelines, model deployment systems, and monitoring platforms. They ensure AI models run reliably in production with automated retraining and scaling.
D\u00e8s $12-18/hrMise en relation sous 48hProfessionnels v\u00e9rifi\u00e9s
Comp\u00e9tences cl\u00e9s
Chaque MLOps Engineer sur la plateforme AI10 est v\u00e9rifi\u00e9 pour ces comp\u00e9tences essentielles.
CI/CD for ML Pipelines
Kubernetes & Docker
Model Monitoring & Observability
Cloud Infrastructure (Terraform, Pulumi)
Tarification
Tarification transparente et abordable pour les meilleurs talents IA.
\u00c0 partir de
$12-18/hr
- Professionnels v\u00e9rifi\u00e9s et test\u00e9s
- Horaire flexible ou par projet
- Mise en relation sous 48 heures
- Gestion de projet d\u00e9di\u00e9e
- Garantie de remplacement
FAQ
Questions fréquemment posées
What does an MLOps Engineer do?
MLOps Engineers bridge the gap between model development and production. They build automated training pipelines, model versioning systems, deployment infrastructure, monitoring dashboards, and auto-retraining workflows — ensuring models run reliably at scale.
When do I need an MLOps Engineer?
When you have models in notebooks that need to go to production, when model deployments are manual and error-prone, when you can't track which model version is running, or when model performance degrades without anyone noticing. MLOps brings engineering rigor to ML.
What MLOps platforms do your engineers work with?
Our engineers work with MLflow, Kubeflow, Weights & Biases, DVC, Airflow, and cloud-native tools (SageMaker Pipelines, Vertex AI, Azure ML). They select the platform that matches your scale, team size, and cloud provider.
How does MLOps reduce model maintenance costs?
Automated pipelines eliminate manual deployment steps (60% time savings). Monitoring catches performance issues before they impact business (preventing costly silent failures). Auto-retraining keeps models accurate as data evolves — without manual intervention.