Bachelors degree or higher in Computer Science Engineering Mathematics Statistics or a related experience designing and managing ML workflows in Databricks utilizing both provisioned clusters and serverless compute resources on advanced programming skills in Python and SQL for data engineering and pipeline using MLflow for experiment tracking model management and deployment within knowledge of ML frameworks libraries and technologies (e.g. TensorFlow PyTorch scikit-learn XGBoost MLflow).Hands-on experience provisioning and optimizing compute resources for ML with workflow orchestration tools (e.g. Airflow Lakeflow Jobs) for pipeline automation and experience operationalizing models and pipelines using GitHub for CI/CD version control and workflow knowledge of FinOps for cloud cost optimization. Effective collaboration and communication skills across technical and non-technical teams.
Bachelors degree or higher in Computer Science Engineering Mathematics Statistics or a related experience designing and managing ML workflows in Databricks utilizing both provisioned clusters and serverless compute resources on advanced programming skills in Python and SQL for data engineering and...
Bachelors degree or higher in Computer Science Engineering Mathematics Statistics or a related experience designing and managing ML workflows in Databricks utilizing both provisioned clusters and serverless compute resources on advanced programming skills in Python and SQL for data engineering and pipeline using MLflow for experiment tracking model management and deployment within knowledge of ML frameworks libraries and technologies (e.g. TensorFlow PyTorch scikit-learn XGBoost MLflow).Hands-on experience provisioning and optimizing compute resources for ML with workflow orchestration tools (e.g. Airflow Lakeflow Jobs) for pipeline automation and experience operationalizing models and pipelines using GitHub for CI/CD version control and workflow knowledge of FinOps for cloud cost optimization. Effective collaboration and communication skills across technical and non-technical teams.
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