MLOps Engineering Director
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The role
Line 1: Lead and scale the MLOps function for a Security Solutions Data Science team, developing production-ready AI/ML models for fraud detection.
Line 2: Oversee Databricks clusters and workspaces; deliver core MLOps capabilities including model monitoring, feature catalog, and experiment tracking.
Line 3: Own CI/CD pipelines to gate the model lifecycle from development to production, and manage MLOps solutions—open-source or third-party vendors.
Line 4: Build and maintain executive dashboards reflecting metrics across model build, deploy, and run phases; ensure data governance and compliance.
Line 5: Manage a team of MLOps engineers; collaborate with data scientists, engineers, and stakeholders to deliver scalable, repeatable pipelines.
Line 6: Requirements include an MS in CS/SE or similar, deep experience with Databricks and Spark, cloud operations, APIs, MLOps tooling (MLFlow, Comet, WandB), data governance, and strong Python/Java/C++ and SQL capabilities; solid DevOps/SRE background and excellent communication.
Line 2: Oversee Databricks clusters and workspaces; deliver core MLOps capabilities including model monitoring, feature catalog, and experiment tracking.
Line 3: Own CI/CD pipelines to gate the model lifecycle from development to production, and manage MLOps solutions—open-source or third-party vendors.
Line 4: Build and maintain executive dashboards reflecting metrics across model build, deploy, and run phases; ensure data governance and compliance.
Line 5: Manage a team of MLOps engineers; collaborate with data scientists, engineers, and stakeholders to deliver scalable, repeatable pipelines.
Line 6: Requirements include an MS in CS/SE or similar, deep experience with Databricks and Spark, cloud operations, APIs, MLOps tooling (MLFlow, Comet, WandB), data governance, and strong Python/Java/C++ and SQL capabilities; solid DevOps/SRE background and excellent communication.
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