2 of 2 Problem-Solving Jobs in Erskine

lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability to work independently or in a team. Collaborate with cross-functional teams to integrate AI solutions into scalable products Ensure best practices in data engineering … experience in PySpark and SQL Deep understanding of ML lifecycle management and CI/CD best practices Familiarity with cloud-native ML platforms and scalable deployment strategies Excellent problem-solving skills and ability to work independently or in a team. Typically, 5+ years of relevant work experience in industry, with a minimum of 2+ years in a similar More ❯

lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability to work independently or in a team. Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras … lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability to work independently or in a team. Typically, 6+ years of relevant work experience in industry, with a minimum of 2+ years in a similar More ❯