Kubeflow

MLOps and AI Engineering bridge the gap between experimental machine learning models and reliable production systems that businesses can depend on. While data scientists create promising models in notebooks, MLOps engineers ensure those models run efficiently, scale properly, and continue performing well in the real world. This domain covers model deployment, monitoring, versioning, automated retraining, and the infrastructure that makes AI systems robust and maintainable. If you enjoy both the technical challenges of software engineering and the excitement of artificial intelligence, MLOps is the critical discipline that makes AI actually work in practice.

Owns the infrastructure that gets models trained, deployed and monitored reliably. A named priority hire in Indian GCCs.