For more than 20 years, our global network of passionate technologists and pioneering craftspeople has delivered cutting-edge technology and game-changing consulting to companies on the brink of AI-driven digital transformation. Since 2001, we have grown into a full-service digital consulting company with 5500+ professionals working on a worldwide ambition.

Driven by the desire to make a difference, we keep innovating. Fueling the growth of our company with our knowledge worker culture. When teaming up with Xebia, expect in-depth expertise based on an authentic, value-led, and high-quality way of working that inspires all we do.

About the Role

We are looking for a skilled and pragmatic Machine Learning Engineer to join our Data & AI team at Xebia. In this role, you will design, develop, and deploy scalable machine learning solutions that drive real-world impact. You will collaborate with data scientists, engineers, and product teams to turn prototypes into production-grade systems. This is a great opportunity for someone passionate about applied machine learning, who values clean, efficient code and understands how to make ML systems robust and maintainable in production.

What You’ll Do

Translate business objectives into data science problems, selecting appropriate algorithms and evaluation strategies.
Design and manage experiments to validate model performance and iterate on improvements.
Collaborate with data engineers to build robust and scalable data pipelines for model training and inference.
Design feature stores and pipelines that support reproducibility, traceability, and version control.
Ensure high data quality through validation, cleansing, and transformation techniques.
Deploy and monitor machine learning models in production using frameworks like MLflow, SageMaker, Vertex AI, or Kubeflow.
Build CI/CD workflows for ML systems to support retraining, testing, and versioning.
Develop APIs or services for real-time inference and integrate models into user-facing applications.
Implement monitoring solutions to track model performance, data drift, and service availability.
Conduct regular audits and retraining to ensure models remain accurate and unbiased over time.
Automate testing of data pipelines, features, and ML components for regression and reproducibility.
Partner with cross-functional teams to ensure ML solutions are aligned with business and product goals.
Participate in peer reviews, architecture discussions, and technical documentation.
Support internal knowledge-sharing initiatives and mentor junior engineers or data scientists

What You Bring

5+ years of experience in a Machine Learning Engineer or Applied ML role.
Experience working as a Data Scientist or Data Engineer in the past.
Strong programming skills in Python and proficiency with data science libraries (pandas, NumPy, scikit-learn, etc.).
Experience training and tuning models for real-world applications using frameworks like TensorFlow, PyTorch, or similar.
Solid understanding of machine learning principles, algorithm selection, and evaluation metrics.
Hands-on experience deploying ML models to production environments (batch and/or real-time).
Familiarity with MLOps practices, including version control (Git), CI/CD, containerization (Docker), and orchestration tools (Kubernetes, Airflow).
Knowledge of cloud platforms (AWS, GCP, or Azure) and their ML/AI toolkits.
Experience working with large-scale data sets and building scalable ML pipelines.
Excellent communication skills in English, both verbal and written

Nice to have:

Experience with NLP, computer vision, or recommendation systems.
Knowledge of fairness, explainability, or interpretability in ML models.
Exposure to experiment tracking tools like MLflow, Weights & Biases, or Neptune.ai.
Familiarity with data lakehouse architectures and modern data stacks (e.g., Delta Lake, Snowflake).
Experience contributing to open-source ML projects or publishing research.

What We Offer