Senior ML Engineer - Recs & Forecasting Platform Lead

Xebia Group; Polska; Hybrid; PLN 200,000 - 340,000; Full time

Job summary

Xebia Group in Poland is seeking an experienced ML Engineer to own and productionize ML solutions across our Azure-based stack. You will be responsible for end-to-end efforts from data handling to model deployment and monitoring.

You will build training and inference pipelines, integrate with a data warehouse and Power BI, ensure reproducibility, and collaborate with BI and business stakeholders during a 15-week MVP phase. Experience in MLOps and cloud ML tooling is essential.

Qualifications

• 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 and automated retraining.
• Experience building and maintaining scheduled batch ML pipelines.
• Ability to take ownership of an existing ML codebase and continue development after knowledge transfer.

Responsibilities

• Take ownership of an existing ML solution and codebase and continue development.
• Develop and productionize ML models for new-product recommendations.
• Build and maintain training and inference pipelines using Azure ML.
• Implement data quality checks and model validation gates.
• Collaborate with BI and business stakeholders to ensure reliable production rollout.

Skills

Azure ML

MLOps

Python

Data pipelines

Model deployment

Tools

Git

Power BI

Azure SQL

GitHub Actions

Azure DevOps

Job description

Xebia Group in Poland is seeking an experienced ML Engineer to own and productionize ML solutions across our Azure-based stack. You will be responsible for end-to-end efforts from data handling to model deployment and monitoring.

You will build training and inference pipelines, integrate with a data warehouse and Power BI, ensure reproducibility, and collaborate with BI and business stakeholders during a 15-week MVP phase. Experience in MLOps and cloud ML tooling is essential.