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, 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.

• strong software engineering fundamentals and experience writing production-quality, modular and maintainable code,
• proven experience deploying and operating machine learning models in production environments,
• hands-on experience with automated monitoring, alerting and production troubleshooting,
• advanced knowledge of MLOps, including model lifecycle automation, CI/CD pipelines and release management,
• practical experience with cloud platforms, preferably Google Cloud Platform (GCP), supporting large-scale data and ML workloads,
• strong Python and SQL skills in machine learning engineering contexts, including training orchestration and inference support,
• experience with containerised services and production deployment environments,
• solid understanding of DevOps principles and SRE practices applied to machine learning systems,
• familiarity with AI-assisted coding tools and their practical application in software development,
• ability to collaborate effectively with Data Science, Research and other engineering teams,
• availability to work in a hybrid model from Warsaw and participate in in-person project meetings when required.

• experience in AdTech, advertising technology, ranking or recommendation systems,
• experience with distributed data processing and feature engineering pipelines, e.g. Vertex Pipelines, Dask or Spark,
• familiarity with cloud-based model serving ecosystems and microservice architectures,
• experience with cross-language model integration patterns,
• knowledge of feature stores and train/serve consistency controls,
• hands-on experience with GCP services such as Vertex AI, BigQuery and Cloud Storage.

Recruitment Process

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

• building, deploying and maintaining production-grade machine learning services and specialised tooling for models in the advertising domain,
• designing and orchestrating ML engineering workflows, including model packaging, deployment strategies, observability and operational stability,
• productionising machine learning models in close collaboration with Data Science and Research teams,
• developing and maintaining automated deployment pipelines, model monitoring, alerting and rollback strategies,
• ensuring reproducible model packaging and reliable model lifecycle management,
• supporting production ML systems through troubleshooting, root cause analysis and continuous improvements,
• applying DevOps and Site Reliability Engineering (SRE) principles to improve the reliability, scalability and maintainability of ML services,
• using AI-assisted coding tools to improve engineering productivity while maintaining high code quality.