Senior DS/ML Engineer New Bulgaria; Poland; Romania; Sweden

Xebia Group; Polonia; Presencial; PLN 200.000 - 320.000; Jornada completa

Descripción de la vacante

Xebia Group in Poland is seeking an experienced ML Engineer to own end-to-end model development, evaluation, and deployment. You will work with Python/SQL, BigQuery, and modern ML frameworks to build ranking, classification, and deployment pipelines.

You will design offline evaluations, run A/B tests, and help productionize models with Airflow, Spark, Kubernetes, and related MLOps tooling. Fluency in data-driven storytelling and collaboration with product teams is essential.

Formación

• Several years of hands-on experience building and shipping machine-learning models to production.
• Strong Python and SQL skills, with experience working with large-scale data in BigQuery or a similar data warehouse.
• Experience with gradient boosting and ranking, and deep learning using TensorFlow/Keras or PyTorch, including training on GPUs.
• Experience designing offline evaluations and running A/B tests.
• Experience with data and ML pipelines using Airflow, dbt and Spark, or running training jobs on Kubernetes.

Responsabilidades

• Own models end to end across caller identity, business identification, spam and fraud detection: problem framing, data, training, evaluation, deployment and monitoring.
• Work on ranking, classification, graph, personalisation and LLM-based models.
• Set the team’s evaluation standards through backtests, baselines, false-positive checks, shadow runs and A/B tests.
• Build shared tooling for evaluation, experiment tracking, and production workflows from notebooks to code.
• Collaborate with product stakeholders to turn problems into modelling goals and success metrics, and explain results.
• Share responsibility for model monitoring and incident handling, including drift detection.
• Document decisions and results in short, reproducible analyses.

Conocimientos

Python

SQL

ML models

A/B testing

BigQuery

TensorFlow

PyTorch

Airflow

dbt

Spark

Kubernetes

Herramientas

Airflow

dbt

Spark

Kubernetes

MLflow

Descripción del empleo

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 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.

You will be:

• Owning models end to end across caller identity, business identification, spam and fraud detection: problem framing, data, training, evaluation, deployment and monitoring.
• Working on ranking, classification, graph, personalisation and LLM-based models.
• Setting the team’s evaluation standards through historical backtests, honest baselines, false-positive checks on legitimate callers, shadow runs and A/B tests.
• Building 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.
• Working 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.
• Sharing responsibility for model monitoring and incident handling, including drift detection.
• Documenting decisions and results in short, reproducible analyses.

Your profile:

• Several years of hands-on experience building and shipping machine-learning models to production.
• Strong Python and SQL skills, with experience working with large-scale data in BigQuery or a similar data warehouse.
• Experience with classical machine learning, such as gradient boosting and ranking, and deep learning using TensorFlow/Keras or PyTorch, including training on GPUs.
• Experience designing offline evaluations that predicted online results, and running A/B tests.
• Experience with data and ML pipelines using Airflow, dbt and Spark, or with running training jobs on Kubernetes.

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

Nice to have:

• Experience gathering requirements from non-technical stakeholders, turning them into a scoped plan, and communicating results and trade-offs clearly.
• Experience setting or improving data science standards within a team, including evaluation, reviews and reproducibility.
• Experience 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.
• Experience using LLMs in production for extraction, classification or generation, including LLM-as-judge evaluation.
• Familiarity with MLOps tools such as MLflow for experiment tracking, model registries, versioning and reproducible training.
• Experience with NLP on short, multilingual or transliterated text, such as names, or computer vision on user images.
• Experience with telecom or calling data, or in fraud detection.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision