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Strong employer brand, metro location, and a mid-level generalist ML role increase applicant competition.
Core ML engineering and MLOps skills transfer well, but enterprise platform specifics moderately limit cross-industry fit.
Requires production ML, MLOps, Azure ML Studio, cloud deployment and specific ML libraries, indicating strict technical filters.
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Lead end-to-end development and operationalization of advanced machine learning models including data preparation, feature engineering, training, validation, and optimization.
Design and maintain production-grade ML pipelines using ML Studio with responsibilities for experiment tracking, versioning, and deployment.
Collaborate with data engineering and platform teams to ensure scalable model deployment, monitor production models, and implement improvements while adhering to enterprise AI standards.
Hands-on experience with ML Studio (Azure ML Studio or equivalent) for experimentation, ML pipelines, and deployment.
Strong knowledge of machine learning, deep learning, statistical modeling concepts, and proficiency in Python and ML libraries (scikit-learn, PyTorch, TensorFlow).
Bachelor's degree in Business Analytics, Computer Science, Statistics, or a Master's in Data Science.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building scalable, production-ready ML workflows and automated training/evaluation jobs in cloud environments (Azure/AWS/GCP).
Skilled in translating complex ML models into interpretable business insights and visualizations for cross-functional collaboration.
Capable of operating in a fast-paced innovation environment with strong ownership over ML system lifecycle and adherence to responsible AI quality standards.