Senior ML Engineer

Job Description We are looking for a Senior Machine Learning Engineer with strong experience in the Telecommunications (Telco) domain to design, develop, deploy, and maintain production-ready machine learning solutions. The ideal candidate will have hands-on experience in ML model development, feature engineering, deployment, monitoring, and retraining, with a strong understanding of MLOps practices and the end-to-end machine learning lifecycle. Key Responsibilities - Develop and deploy production-grade machine learning models for Telco use cases. - Build ML solutions for use cases such as customer churn prediction, customer segmentation/clustering, and demand forecasting. - Prepare, clean, transform, and analyze large customer datasets. - Perform feature engineering and develop relevant features for machine learning models. - Train, validate, and evaluate supervised and unsupervised machine learning models. - Use Python for data preparation, model development, validation, and automation. - Use SQL to access, extract, transform, and process data from various sources. - Implement and maintain MLOps pipelines across the ML lifecycle. - Manage model versioning, deployment, production monitoring, and retraining. - Monitor model performance and data/model drift in production and take appropriate corrective actions. - Collaborate with data engineers, data scientists, and business stakeholders to deliver scalable ML solutions. - Ensure ML models are reliable, maintainable, and suitable for production environments. - Continuously improve existing models, features, and ML workflows based on production results. Requirements Requirements - 4+ years of experience in Machine Learning, Data Science, or a related field. - Telco/Telecommunications domain experience is a MUST. - Proven experience building and deploying production ML models for Telco use cases, such as: - Churn prediction - Customer clustering/segmentation - Demand forecasting - Customer behavior prediction - Strong knowledge of supervised and unsupervised machine learning techniques. - Strong hands-on experience with Python for data preparation, feature engineering, and ML model development. - Strong SQL skills for accessing, processing, and analyzing customer data. - Hands-on experience with feature engineering and customer data preparation. - Practical MLOps experience, including: - Model versioning - Model deployment - Production monitoring - Model retraining - Experience with MLflow or an equivalent MLOps/model lifecycle management platform. - Experience taking ML models from development through production deployment and ongoing monitoring. - Ability to evaluate model performance and identify opportunities for model improvement. - Strong understanding of the end-to-end machine learning lifecycle. - Experience with Dataiku or equivalent enterprise ML platforms is a plus. - Experience with Spark, Feature Stores, or uplift modelling is a plus.