We are seeking a Senior Machine Learning Engineer to join our core technical team, bringing expertise in building production‑grade ML systems, data engineering and managing the full lifecycle of machine learning models. This role will contribute to multiple initiatives ensuring impactful solutions for real business challenges. Responsibilities Design and develop machine learning models for forecasting, classification, recommendation, segmentation and optimization Package models for production and expose them via APIs or scheduled jobs Implement monitoring, retraining and lifecycle management for ML solutions Apply MLOps best practices, including model versioning, experiment tracking and reproducible pipelines Monitor model performance in production and propose data‑driven improvements Participate in technical design reviews and propose well‑reasoned solutions with trade‑offs Document architectural decisions and facilitate knowledge transfer to internal teams Champion engineering standards, tools and best practices across the team Collaborate with business stakeholders to translate problems into machine learning solutions Requirements 3+ years of experience in ML Engineering or Data Engineering working with production systems Proven track record of delivering ML models actively used by real users with at least 2 live production projects Proficiency in Python, PySpark and SQL Skills in Scikit‑learn, Databricks (production usage) and Delta Lake Expertise with REST APIs, Git, CI/CD pipelines, Docker and Jenkins Knowledge of MLflow for model versioning and experiment tracking Background in time series forecasting, similarity techniques and computer vision models Competency in feature engineering, model evaluation and monitoring Strong communication skills to collaborate effectively with non‑technical stakeholders Ability to balance model simplicity and complexity with sound judgment English proficiency at B2 level or higher Nice to have Experience in retail, fashion, consumer goods or distribution domains Familiarity with enterprise planning tools like SAP IBP, SAP M3 or SAC Background in building model monitoring dashboards with Power BI, Tableau or Looker Knowledge of semantic similarity or embeddings in product catalogs Understanding of multi‑country or multi‑currency platform challenges Capability to design Lakehouse architectures such as Medallion or Data Mesh We offer International projects with top brands Work with global teams of highly skilled, diverse peers Employee financial programs Paid time off and sick leave Upskilling, reskilling and certification courses Unlimited access to the LinkedIn Learning library and 22,000+ courses Global career opportunities Volunteer and community involvement opportunities EPAM Employee Groups Award‑winning culture recognized by Glassdoor, Newsweek and LinkedIn #J-18808-Ljbffr