Senior ML Engineer

• 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.

• 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.

Recruitment Process

CV review – HR call – Interview – Client Interview – Decision

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.

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.