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, Pa...
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