Summary: Seeking a Lead Machine Learning Engineer to own end-to-end model delivery and MLOps rigor, creating and operationalizing forecasting, recommendation, and optimization models. Highlights: 1. Lead end-to-end ML model delivery and MLOps rigor 2. Design and build diverse machine learning models for production 3. Promote engineering standards and collaborate with stakeholders 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. **Responsibilities** * Design and build machine learning models for forecasting, classification, recommendation, segmentation and optimization * Package models for production use and deliver them through 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 * Track model behavior in production and recommend data\-driven improvements * Contribute to technical design reviews and present well\-reasoned options with trade\-offs * Document architecture decisions and enable knowledge transfer to internal teams * Promote engineering standards, tools and best practices across the team * Collaborate with business stakeholders to translate problems into machine learning solutions **Requirements** * Proven 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 projects * High proficiency in Python, PySpark and SQL * Practical skills with Scikit\-learn, Databricks (production usage) and Delta Lake * Strong expertise with REST APIs, Git, CI/CD pipelines, Docker and Jenkins * Working knowledge of MLflow for model versioning and experiment tracking * Solid background in time series forecasting, similarity techniques and computer vision models * Deep understanding of feature engineering, model evaluation and monitoring * Excellent communication skills to partner effectively with non\-technical stakeholders * Sound judgment to balance model simplicity versus complexity appropriately * English proficiency at B2 (Upper\-Intermediate) level or higher **Nice to have** * Experience across retail, fashion, consumer goods or distribution domains * Familiarity with enterprise planning tools such as SAP IBP, SAP M3 or SAC * Exposure to building model monitoring dashboards using Power BI, Tableau or Looker * Knowledge of semantic similarity or embeddings in product catalogs * Understanding of multi\-country or multi\-currency platform challenges * Ability to design Lakehouse architectures, including Medallion or Data Mesh