Senior ML Engineer

Job Description

We are looking for aSenior Machine Learning Engineerwith strong experience in theTelecommunications (Telco) domainto design, develop, deploy, and maintain production-ready machine learning solutions.

The ideal candidate will have hands-on experience inML 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 forTelco use cases.
• Build ML solutions for use cases such ascustomer churn prediction, customer segmentation/clustering, and demand forecasting.
• Prepare, clean, transform, and analyze large customer datasets.
• Performfeature engineeringand develop relevant features for machine learning models.
• Train, validate, and evaluate supervised and unsupervised machine learning models.
• UsePythonfor data preparation, model development, validation, and automation.
• UseSQLto access, extract, transform, and process data from various sources.
• Implement and maintainMLOps pipelinesacross the ML lifecycle.
• Managemodel 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 experiencein Machine Learning, Data Science, or a related field.
• Telco/Telecommunications domain experience is a MUST.
• Proven experience building and deployingproduction ML models for Telco use cases, such as:
• Churn prediction
• Customer clustering/segmentation
• Demand forecasting
• Customer behavior prediction
• Strong knowledge ofsupervised and unsupervised machine learningtechniques.
• Strong hands-on experience withPythonfor data preparation, feature engineering, and ML model development.
• StrongSQLskills for accessing, processing, and analyzing customer data.
• Hands-on experience withfeature engineeringand customer data preparation.
• PracticalMLOps experience, including:
• Model versioning
• Model deployment
• Production monitoring
• Model retraining
• Experience withMLflow or an equivalent MLOps/model lifecycle management platform.
• Experience taking ML models from development throughproduction deployment and ongoing monitoring.
• Ability to evaluate model performance and identify opportunities for model improvement.
• Strong understanding of the end-to-endmachine learning lifecycle.
• Experience withDataiku or equivalent enterprise ML platformsis a plus.
• Experience withSpark, Feature Stores, or uplift modellingis a plus.