Senior MLOps Engineer

7+ years of overall experience in Infrastructure or Platform Engineering, with 3+ years focused deeply on hands-on production MLOps architectures.

Strong Kubernetes expertise: Deep hands-on experience running and scaling ML workloads on K8s and cloud environments.

Hands-on MLOps Stack: Experience building or maintaining infrastructure using tools for model serving, orchestration, or monitoring (e.g., working with technologies like Kubernetes, ArgoCD, MLflow/ClearML, Dagster/Airflow, Kserve, or Evidently).

Real-Time Model Serving: Practical experience taking models out of data science environments and operationalizing them for low-latency, real-time production serving.

Stron g Individual Contributor: You thrive as a hands-on technical expert. You enjoy owning the architecture, engineering the platform, and driving technical alignment across teams.