ML Ops / Data Engineer

Experience deploying ML models to production at scale

 Hands-on with MLOps tools – MLflow, Kubeflow,
SageMaker, or similar
 Experience with CI/CD, Docker, and Kubernetes
 Strong in Python, SQL, and scripting
 Working knowledge of cloud platforms – AWS / Azure /
GCP
 Data pipeline development with Airflow, Spark, or similar
 Nice to Have:
 Exposure to feature stores and model versioning tools
 Experience with data quality monitoring and model drift
detection
 Prior work in a product-based company