Machine Learning Engineer

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
• Support technical evaluations of other consultants when required, contributing to the assessment of skills and alignment with project needs

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
• 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

• 100% remote work to provide flexibility and work-life balance.
• Company laptop and necessary equipment to perform your role effectively.
• Competitive salary and benefits package aligned with local market benchmarks