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.
You will be:
- developing scalable ML solutions by applying software engineering best practices and transforming exploratory code into modular, reusable, and testable Python packages,
- implementing experiment tracking, reproducibility, and versioning practices to ensure traceability of ML workflows and results,
- designing and building distributed training pipelines with robust checkpointing, fault tolerance, and standardized model evaluation frameworks,
- creating production-ready model packaging, serving, and deployment solutions, including versioned containers, canary releases, rollback procedures, and online/batch inference,
- building and maintaining ML monitoring and retraining pipelines covering data drift detection, prediction quality monitoring, and continuous model evaluation,
- defining and enforcing ML lifecycle governance, including observability, documentation, operational runbooks, and model retirement processes,
- collaborating with cross-functional teams while taking ownership of ML engineering deliverables and promoting security-first engineering practices.
Your profile:
- proven experience as an ML Engineer in production environments,
- strong proficiency in Python and modern ML frameworks (TensorFlow, PyTorch),
- hands-on experience with: ML lifecycle tooling (MLflow, Vertex AI, or equivalent), distributed training and scalable compute environments, containerization (Docker) and deployment pipelines,
- experience with cloud-native ML platforms, preferably Google Cloud / Vertex AI,
- solid understanding of: model evaluation beyond accuracy (fairness, robustness, monitoring) and CI/CD for ML systems (MLOps practices),
- familiarity with artifact management and version control systems.
Work from the European Union region and a work permit are required.
Nice to have:
- experience building enterprise AI/ML platforms supporting multiple teams/products,
- knowledge of data governance, lineage, and compliance frameworks Exposure to high-scale ML systems and real-time inference architectures,
- experience implementing automated retraining and adaptive learning systems.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
