ML engineer for product recommendations

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.

• take ownership of an existing ML solution and codebase following knowledge transfer from the Lead AI/ML Engineer
• develop and productionize machine-learning models for new-product first-order recommendations
• build and maintain training and inference pipelines using Azure Machine Learning
• implement scheduled model retraining and integrate models with the existing model registry and validation gate
• implement data quality and completeness checks, including validation of product attributes required by the models
• develop recommendation logic accounting for service levels, minimum order quantities, time until the next delivery and category-based fallback rules
• implement and validate the transition of products from the new-item model to the main forecasting model as sufficient sales history becomes available
• extend daily recommendations to eligible catalogue products based on agreed criteria and backtesting results
• support the phased rollout of catalogue products and implement monitoring for individual product segments
• maintain MLOps capabilities, including model versioning, validation, automated retraining, monitoring, data-drift detection and alerting
• integrate ML outputs with the existing data warehouse and Power BI reporting
• ensure production runs are reproducible and that model versions retain their training windows and feature contracts
• participate in testing, dry runs, parallel runs, documentation and operational handover
• collaborate with the Lead AI/ML Engineer, BI team and business stakeholders to ensure a reliable production rollout

• hands-on experience developing and productionizing machine-learning solutions
• practical experience with Azure Machine Learning, including training and inference pipelines
• experience implementing MLOps practices, including model registries, model versioning, automated retraining, validation gates, monitoring and rollback
• experience building and maintaining scheduled batch ML pipelines
• ability to take ownership of an existing ML codebase and continue development following an initial knowledge-transfer phase
• experience integrating ML pipelines with data warehouses
• experience implementing data validation and input-quality checks
• experience with model monitoring and data-drift detection
• experience implementing automated testing and CI/CD for ML workloads
• familiarity with Git-based repositories and deployment workflows
• understanding of model evaluation, backtesting, champion/challenger validation and production acceptance criteria
• ability to document ML systems and operational procedures
• ability to collaborate effectively with technical teams and business stakeholders
• ability to work independently on implementation after an initial knowledge-transfer period
• availability to participate in an approximately 15-week MVP, with an expected commitment of around 45 engineer-days
• Будет плюсом: experience with demand forecasting, inventory optimization or purchasing and replenishment systems; experience with LightGBM; experience working with Azure SQL-based data warehouses; familiarity with Azure managed identities, Key Vault and Azure Monitor; familiarity with Power BI integrations and ML monitoring and reporting use cases; experience with Azure DevOps or GitHub Actions and Azure deployment workflows; experience working with service-level-based inventory or order-quantity models; familiarity with model calibration and monitoring forecast accuracy and bias; experience developing ML solutions for new-product forecasting with limited historical data; familiarity with retail, wholesale, supply chain or catalogue-based business environments

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