ML Engineer

Poland and Central-Eastern Europe; Polska; Hybrid; PLN 180,000 - 260,000; Full time

Job summary

Xebia is a global AI-first, digital transformation, and engineering partner. We deploy and run ML in production, collaborating with DS and Research teams from Warsaw in a hybrid setup.

Our focus on MLOps, SRE, and cloud-native platforms helps customers scale data-driven solutions across multiple industries. Join a team that values engineering excellence, knowledge sharing, and a hands-on approach to modern AI workloads, with opportunities to work across GCP, Vertex AI, BigQuery, and Cloud

Qualifications

• Proven experience deploying and operating ML models in production.
• Hands-on experience with automated monitoring and production troubleshooting.
• Advanced knowledge of MLOps, including model lifecycle automation, CI/CD pipelines, and release management.
• Practical experience with GCP and handling large-scale data/ML workloads.
• Experience with containerised services and production deployment environments.
• Familiarity with AI-assisted coding tools and their use in software development.
• Ability to collaborate with Data Science, Research and other engineering teams.
• Willingness to work in a hybrid model from Warsaw and attend in-person meetings when required.

Responsibilities

• Productionising machine learning models in close collaboration with Data Science and Research teams.
• Supporting production ML systems through troubleshooting, root-cause analysis and continuous improvements.
• Applying DevOps and SRE principles to improve reliability, scalability and maintainability of ML services.

Skills

ML in production

MLOps

CI/CD pipelines

Cloud platforms (GCP)

Containerization

Monitoring & troubleshooting

Collaboration with DS/Research

Tools

Vertex AI

BigQuery

Cloud Storage

Job description

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:

• productionisingmachinelearningmodelsinclosecollaborationwithDataScienceandResearchteams,
• supportingproductionMLsystemsthroughtroubleshooting,rootcauseanalysisandcontinuousimprovements,
• applyingDevOpsandSiteReliabilityEngineering(SRE)principlestoimprovethereliability,scalabilityandmaintainabilityofMLservices,

Yourprofile:

• provenexperiencedeployingandoperatingmachinelearningmodelsinproductionenvironments,
• hands-onexperiencewithautomatedmonitoring,alertingandproductiontroubleshooting,
• advancedknowledgeofMLOps,includingmodellifecycleautomation,CI/CDpipelinesandreleasemanagement,
• practicalexperiencewithcloudplatforms,preferablyGoogleCloudPlatform(GCP),supportinglarge-scaledataandMLworkloads,
• experiencewithcontainerisedservicesandproductiondeploymentenvironments,
• familiaritywithAI-assistedcodingtoolsandtheirpracticalapplicationinsoftwaredevelopment,
• abilitytocollaborateeffectivelywithDataScience,Researchandotherengineeringteams,
• availabilitytoworkinahybridmodelfromWarsawandparticipateinin-personprojectmeetingswhenrequired.

Nicetohave:

• experiencewithdistributeddataprocessingandfeatureengineeringpipelines,e.g.VertexPipelines,DaskorSpark,
• familiaritywithcloud-basedmodelservingecosystemsandmicroservicearchitectures,
• experiencewithcross-languagemodelintegrationpatterns,
• knowledgeoffeaturestoresandtrain/serveconsistencycontrols,
• hands-onexperiencewithGCPservicessuchasVertexAI,BigQueryandCloudStorage.

Recruitment Process:

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