aimlroles

Senior ML Engineer

SSC HR Solutions Open · verified Oct 22, 2026
Remote Cairo Senior Machine Learning Engineering

At a glance

Senior Machine Learning Engineering role at SSC HR Solutions. Remote, Egypt.

Pay not stated ML Engineer salary

RoleMachine Learning Engineering
SenioritySenior
LocationCairo
WorkplaceRemote
PostedOct 4, 2026 · 2w ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build and launch production ML models for telco needs such as churn, customer groups and demand forecasts
  • Prepare customer data, create model features, then train and assess supervised and unsupervised models
  • Use Python and SQL to prepare, transform and analyze data and automate model work
  • Run model lifecycle pipelines, including version control, deployment, production checks and retraining
  • Track model and data drift, correct issues and refine models using production results

What you bring

  • At least four years in machine learning, data science or a related area, plus telecommunications experience
  • A track record of deploying production ML for telco use cases such as churn, segmentation or forecasting
  • Strong practical Python and SQL skills, including customer-data preparation and feature creation
  • Hands-on MLOps experience covering model releases, deployment, monitoring and retraining, using MLflow or an equivalent platform

Who this fits

This senior role suits an engineer who has taken telco machine-learning models from development into production and maintained them over time. You’ll work with data engineers, data scientists and business stakeholders. The role is remote and based in Cairo, Egypt.

From the employer

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.