Senior ML Engineer

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.

Desired Candidate Profile

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.