Job Description
Description
Develop production-ready implementations of proposed solutions across different ML and DL algorithms, including testing on customer data to improve efficacy, and robustness.Research and test novel machine learning approaches for analysing large-scale distributed computing applications. Prepare reports, visualizations, and presentations to communicate findings effectively.End-to-End ML Ops Lifecycle: Implement and manage the full ML Ops lifecycle using tools such as Kubeflow, MLflow, AutoML, and Kserve for model deployment.Model Implementation: Develop and deploy the machine learning models using Keras, PyTorch, TensorFlow ensuring high performance and scalability.Distributed Systems: Run and manage PySpark and Kafka on distributed systems with large-scale, non-linear network elements.Proficient in Python programming and experienced with machine learning libraries such as Scikit-Learn and NumPy, Pandas.
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