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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.\n\nWe 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.\n\nIn the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, 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.\n\nYou will be:\n\nOwning models end to end across caller identity, business identification, spam and fraud detection: problem framing, data, training, evaluation, deployment and monitoring.\n\nWorking on ranking, classification, graph, personalisation and LLM-based models.\n\nSetting the team’s evaluation standards through historical backtests, honest baselines, false-positive checks on legitimate callers, shadow runs and A/B tests.\n\nBuilding shared tooling that makes good practice the easy path, including reusable evaluation harnesses, experiment tracking, project templates and workflows for moving work from notebooks into production code.\n\nWorking with product stakeholders to turn loosely defined problems into clear modelling goals and success metrics, and explaining results and trade-offs to non-technical audiences.\n\nSharing responsibility for model monitoring and incident handling, including drift detection.\n\nDocumenting decisions and results in short, reproducible analyses.\n\nYour profile:\n\nSeveral years of hands-on experience building and shipping machine-learning models to production.\n\nStrong Python and SQL skills, with experience working with large-scale data in BigQuery or a similar data warehouse.\n\nExperience with classical machine learning, such as gradient boosting and ranking, and deep learning using TensorFlow/Keras or PyTorch, including training on GPUs.\n\nExperience designing offline evaluations that predicted online results, and running A/B tests.\n\nExperience with data and ML pipelines using Airflow, dbt and Spark, or with running training jobs on Kubernetes.\n\nWork from the European Union region and a work permit are required.\n\nNice to have:\n\nExperience gathering requirements from non-technical stakeholders, turning them into a scoped plan, and communicating results and trade-offs clearly.\n\nExperience setting or improving data science standards within a team, including evaluation, reviews and reproducibility.\n\nExperience with graph machine learning, such as GNNs, link prediction or label propagation, or with recommendation and retrieval models, such as two-tower models and embeddings.\n\nExperience using LLMs in production for extraction, classification or generation, including LLM-as-judge evaluation.\n\nFamiliarity with MLOps tools such as MLflow for experiment tracking, model registries, versioning and reproducible training.\n\nExperience with NLP on short, multilingual or transliterated text, such as names, or computer vision on user images.\n\nExperience with telecom or calling data, or in fraud detection.\n\nRecruitment Process:\n\nCV review – HR call – Interview – Client Interview – 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