{"id":1835082,"url":"https://alion.io/job/ssc-hr-solutions-senior-ml-engineer-2","title":"Senior ML Engineer","company":{"id":3460,"name":"SSC HR Solutions","domain":"sschrsolutions.com","url":"https://alion.io/company/sschrsolutions","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":13,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":80,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cairo, Egypt"],"countries":["EG"],"hiring_countries":["EG"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":93000,"max_usd":241000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2216},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Feature Store","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-04T13:34:26Z","employer_posted_date":"2026-10-04","last_verified_at":"2026-10-20T15:59:33Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"Job Description\nWe 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.\nThe 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.\nKey Responsibilities\nDevelop and deploy production-grade machine learning models for Telco use cases.\nBuild ML solutions for use cases such as customer churn prediction, customer segmentation/clustering, and demand forecasting.\nPrepare, clean, transform, and analyze large customer datasets.\nPerform feature engineering and develop relevant features for machine learning models.\nTrain, validate, and evaluate supervised and unsupervised machine learning models.\nUse Python for data preparation, model development, validation, and automation.\nUse SQL to access, extract, transform, and process data from various sources.\nImplement and maintain MLOps pipelines across the ML lifecycle.\nManage model versioning, deployment, production monitoring, and retraining.\nMonitor model performance and data/model drift in production and take appropriate corrective actions.\nCollaborate with data engineers, data scientists, and business stakeholders to deliver scalable ML solutions.\nEnsure ML models are reliable, maintainable, and suitable for production environments.\nContinuously improve existing models, features, and ML workflows based on production results.\nRequirements\nRequirements\n4+ years of experience in Machine Learning, Data Science, or a related field.\nTelco/Telecommunications domain experience is a MUST.\nProven experience building and deploying production ML models for Telco use cases, such as:\nChurn prediction\nCustomer clustering/segmentation\nDemand forecasting\nCustomer behavior prediction\nStrong knowledge of supervised and unsupervised machine learning techniques.\nStrong hands-on experience with Python for data preparation, feature engineering, and ML model development.\nStrong SQL skills for accessing, processing, and analyzing customer data.\nHands-on experience with feature engineering and customer data preparation.\nPractical MLOps experience, including:\nModel versioning\nModel deployment\nProduction monitoring\nModel retraining\nExperience with MLflow or an equivalent MLOps/model lifecycle management platform.\nExperience taking ML models from development through production deployment and ongoing monitoring.\nAbility to evaluate model performance and identify opportunities for model improvement.\nStrong understanding of the end-to-end machine learning lifecycle.\nExperience with Dataiku or equivalent enterprise ML platforms is a plus.\nExperience with Spark, Feature Stores, or uplift modelling is a 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