Senior Machine Learning Engineer

Role Overview: As a Machine Learning Engineer, you will play a key role in developing and enhancing a Telecom Artificial Intelligence Product using cutting-edge technologies. Your focus will be on implementing advanced algorithms and models to solve complex problems related to anomaly detection, forecasting, event correlation, and fraud detection. Key Responsibilities: - Develop production-ready implementations of proposed solutions across various machine learning and deep learning algorithms, including testing on live customer data to enhance efficacy and robustness. - Research and test novel machine learning approaches for analyzing large-scale distributed computing applications. - Implement and manage the full machine learning operations (ML Ops) lifecycle using tools like Kubeflow, MLflow, AutoML, and Kserve for model deployment. - Develop and deploy machine learning models using PyTorch, TensorFlow to ensure high performance and scalability. - Run and manage PySpark and Kafka on distributed systems with large-scale, non-linear network elements. - Experiment with multiple algorithms, optimizing hyperparameters to identify the best-performing models. - Continuously gather feedback from users, retrain models, and update them to maintain and improve performance. - Quickly understand network characteristics, especially in RAN and CORE domains, to provide exploratory data analysis on network data. - Apply GAN AI techniques to address network-related use cases and challenges. - Collaborate with data scientists, software engineers, and stakeholders to integrate implemented systems into the SaaS platform. - Suggest innovative concepts to improve the overall platform and create use cases for solving business problems. - Experience with MySQL/NoSQL databases and deploying end-to-end machine learning models in production. Qualification Required: - Bachelor's degree in Science/IT/Computing or equivalent. - 4+ years of experience in a QA Engineering role. - Strong quantitative and applied mathematical skills with in-depth knowledge of statistical techniques and machine learning methods. - Certification courses in Data Science/Machine Learning are advantageous. - Experience with Telecom Product development preferred, along with familiarity with TMF standards. - Proficiency in Python programming and machine learning libraries such as Scikit-Learn and NumPy. - Knowledge of DL frameworks like Keras, TensorFlow, and PyTorch, as well as Big Data tools and environments. (Note: Additional details of the company are not provided in the job description.),