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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.