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. Good understanding of time series analysis, data mining, text mining, and creating data architectures. Processing Approaches: Utilize both batch processing and incremental approaches to manage and analyse large datasets. Conduct data preprocessing, feature engineering, and exploratory data analysis (EDA). Algorithm Experimentation: Experiment with multiple algorithms, optimizing hyperparameters to identify the best-performing models. Cloud Knowledge: Execute machine learning algorithms in cloud environments, leveraging cloud resources effectively. Model Retraining: Continuously gather feedback from users, retrain models, and update them to maintain and improve performance, optimizing model inference times. Network Domain Expertise: Quickly understand network characteristics, especially in RAN and CORE domains, to provide exploratory data analysis (EDA) on network data.