MLOps Engineer
A specialised ML Engineer focused on infrastructure, CI/CD, monitoring, and reliability for ML systems.
What is MLOps Engineer?
MLOps Engineer is to ML what DevOps Engineer is to web services. They build the pipelines that train, evaluate, deploy, monitor, and retrain models continuously. Their work is invisible when it goes well and very visible when it does not.
Core tools in 2026: **MLflow** (experiment tracking + model registry), **Kubeflow** or **SageMaker** (training pipelines), **Docker + Kubernetes** (deployment), **Prometheus + Grafana** (monitoring), **GitHub Actions** or **Argo Workflows** (CI/CD).
Salary in India: roughly the same as ML Engineer — ₹12–25 LPA entry, up to ₹50+ LPA senior. The role is newer and rarer than ML Engineer; demand outstrips supply.
MLOps is the gap that breaks most ML projects. Companies that have data scientists building models but no MLOps engineers usually fail to ship.
An MLOps Engineer at Fractal built the platform on which 200+ data scientists train and deploy models. The platform handles 3,000+ training jobs per month and 50,000+ inference endpoints — all managed by a team of 8 MLOps engineers.
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