AI/ML Engineer

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. In the CEE region, the team of nearly 1,000 experts delivers modern applications, data platforms, and AI solutions for clients across various industries. They work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake combining strong engineering culture with a consulting mindset.

• 3–6 years of professional experience in Machine Learning / AI Engineering
• Strong Python programming skills
• Hands-on experience with PyTorch and/or TensorFlow and scikit-learn
• Experience building and deploying machine learning models in production environments
• Good understanding of model evaluation, experimentation, and performance monitoring
• Experience with ML lifecycle management tools such as MLflow
• Experience with cloud-based ML platforms (preferred Azure ML; also Vertex AI or AWS SageMaker)
• Familiarity with CI/CD concepts and MLOps practices
• Experience working with structured and unstructured data
• Understanding of AI governance, model documentation, and responsible AI principles
• Experience working in cross-functional Agile teams
• Strong analytical and problem-solving skills
• Good communication skills and ability to collaborate with business stakeholders

Nice to have:

• Experience with GenAI solutions and LLM-based applications
• Experience with vector databases and embedding models
• Knowledge of Retrieval-Augmented Generation (RAG) architectures
• Experience with document intelligence and OCR solutions
• Knowledge of Azure OpenAI services
• Experience in automotive, mobility, retail, or dealer-network environments
• Familiarity with blue/green, canary, rolling, or shadow deployment strategies
• Experience supporting AI systems in regulated environments

• Designing and developing machine learning solutions aligned with business objectives
• Building, training, evaluating, and deploying ML models for predictive analytics, recommendation systems, document intelligence, and decision-support use cases
• Owning the ML lifecycle including data preparation, feature engineering, model training, evaluation, deployment, monitoring, and retraining
• Making technical implementation decisions within architectural and engineering standards
• Working with structured and unstructured data
• Collaborating with cross-functional teams to deliver production-ready solutions
• Supporting industrialization activities such as CI/CD, monitoring, observability, and model governance
• Troubleshooting production issues and continuously improving AI solutions
• Contributing to AI governance, responsible AI practices, technical documentation, and transparency requirements
• Participating in knowledge sharing and building AI/ML capabilities within the team

Work from the European Union region and a work permit are required.