We are building production-ready ML solutions and need a Lead Machine Learning Engineer to own end-to-end model delivery and MLOps rigor. You will create forecasting, recommendation, and optimization models, operationalize them with APIs and pipelines, and drive monitoring and continuous improvement—apply now.ResponsibilitiesDesign and build machine learning models for forecasting, classification, recommendation, segmentation and optimizationPackage models for production use and deliver them through APIs or scheduled jobsImplement monitoring, retraining and lifecycle management for ML solutionsApply MLOps best practices, including model versioning, experiment tracking and reproducible pipelinesTrack model behavior in production and recommend data-driven improvementsContribute to technical design reviews and present well-reasoned options with trade-offsDocument architecture decisions and enable knowledge transfer to internal teamsPromote engineering standards, tools and best practices across the teamCollaborate with business stakeholders to translate problems into machine learning solutionsRequirementsProven hands-on experience in ML Engineering or Data Engineering for production systems (5+ years)Demonstrated track record of shipping ML models used by real users, including at least 2 live production projectsHigh proficiency in Python, PySpark and SQLPractical skills with Scikit-learn, Databricks (production usage) and Delta LakeStrong expertise with REST APIs, Git, CI/CD pipelines, Docker and JenkinsWorking knowledge of MLflow for model versioning and experiment trackingSolid background in time series forecasting, similarity techniques and computer vision modelsDeep understanding of feature engineering, model evaluation and monitoringExcellent communication skills to partner effectively with non-technical stakeholdersSound judgment to balance model simplicity versus complexity appropriatelyEnglish proficiency at B2 (Upper-Intermediate) level or higherNice to haveExperience across retail, fashion, consumer goods or distribution domainsFamiliarity with enterprise planning tools such as SAP IBP, SAP M3 or SACExposure to building model monitoring dashboards using Power BI, Tableau or LookerKnowledge of semantic similarity or embeddings in product catalogsUnderstanding of multi-country or multi-currency platform challengesAbility to design Lakehouse architectures, including Medallion or Data Mesh#J-18808-Ljbffr