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 McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, 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.
About the role:
We are looking for a Mid AI/ML Engineer to join the team responsible for the end-to-end delivery of AI and Generative AI solutions - from ideation and experimentation to production and continuous improvement.
In this role, you will work on real-world AI use cases supporting dealer operations, back-office processes, document processing, decision-support systems, and intelligent data platforms.
You will take ownership of machine learning solutions across their lifecycle, working closely with Data Engineers, MLOps Engineers, Solution Architects, and business stakeholders to turn business needs into production-ready AI solutions.
You will be:
- 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 for assigned solutions, including data preparation, feature engineering, model training, evaluation, deployment, monitoring, and retraining,
- making technical implementation decisions within established architectural and engineering standards,
- working with structured and unstructured data,
- collaborating with Data Engineers, MLOps Engineers, Solution Architects, and business stakeholders to deliver production-ready solutions,
- supporting industrialization activities, including CI/CD, monitoring, observability, and model governance,
- troubleshooting production issues and continuously improve existing AI solutions,
- contributing to AI governance, responsible AI practices, technical documentation, and transparency requirements,
- participating in knowledge sharing and helping build AI/ML capabilities within the team.
Your profile:
- 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 - Azure ML preferred; Vertex AI or AWS SageMaker also welcome,
- familiarity with CI/CD concepts and MLOps practices,
- experience working with both 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 the ability to collaborate directly with business stakeholders.
Work from the European Union region and a work permit are required.
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.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
