Description:
ML Ops Engineer to drive the full lifecycle of machine learning solutions from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering. Key Responsibilities Develop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights. Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Drive
Sep 9, 2025;
from:
dice.com