Lead MLOps Engineer
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Lead MLOps Engineer responsible for designing and operating scalable ML pipelines in a Databricks environment, focusing on fraud-detection AI models.
Own and evolve MLOps capabilities including model monitoring, feature cataloging, experiment tracking, and robust CI/CD pipelines that gate ML lifecycle from development to production.
Design and maintain MLOps solutions—whether open-source or vendor-based—and build executive dashboards showing model build, deployment, and runtime metrics.
Provide technical leadership to a team of MLOps engineers while collaborating with Data Scientists to deliver production-ready AI models.
Implement data governance, data ingestion, data integration, and data observability practices; ensure secure, compliant access to data and APIs.
Proficiency in Python and SQL, Spark/Databricks, cloud operations, and DevOps/SRE tooling to deliver scalable, repeatable modeling systems.
Own and evolve MLOps capabilities including model monitoring, feature cataloging, experiment tracking, and robust CI/CD pipelines that gate ML lifecycle from development to production.
Design and maintain MLOps solutions—whether open-source or vendor-based—and build executive dashboards showing model build, deployment, and runtime metrics.
Provide technical leadership to a team of MLOps engineers while collaborating with Data Scientists to deliver production-ready AI models.
Implement data governance, data ingestion, data integration, and data observability practices; ensure secure, compliant access to data and APIs.
Proficiency in Python and SQL, Spark/Databricks, cloud operations, and DevOps/SRE tooling to deliver scalable, repeatable modeling systems.
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