MLOps 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. You will be: owning the machine learning platform infrastructure as part of the Data Infrastructure team, which operates the company-wide GCP data and ML platform, owning Vertex AI, Kubeflow Pipelines, and GKE-based training and serving infrastructure, keeping it reliable, secure, and cost-efficient, building templates, CI/CD, and self-service workflows that enable data scientists to ship models without filing tickets, managing the model lifecycle, including model registry, versioning, deployment, rollback, and monitoring for drift and serving health, supporting feature and training data flows across BigQuery and Airflow, in collaboration with data platform engineers, managing infrastructure through GitOps with Config Sync, applying least-privilege access for PII and policy-tagged data, managing GPU and compute usage, quotas, and per-team spend visibility, taking end-to-end ownership of the platform that vertical teams use to build, train, and serve their models. Your profile: 5+ years in MLOps, ML platform or platform engineering, including at least 2 years owning production ML infrastructure end to end, hands-on production experience with GCP, specifically Vertex AI, GKE and BigQuery, strong Kubernetes expertise, including running and debugging clusters independently, pipeline orchestration with Kubeflow Pipelines or Vertex AI Pipelines, plus Airflow, GitOps for infrastructure (Config Sync or similar) and CI/CD for model deployment, production-grade Python skills, including testing and packaging, a platform mindset: experience building documented, self-service tooling for other teams, with SLOs, ability to work independently as the single owner of a company-wide platform. Work from the European Union region and a work permit are required. Nice to have: streaming features or online inference using Dataflow/Flink and Kafka, feature stores such as Vertex AI Feature Store or Feast, and model monitoring tooling, LLM serving and evaluation on Vertex AI or similar platforms, GPU scheduling and cost optimisation on GKE, Data governance experience (DataHub, policy tags) and privacy regulations such as GDPR or DPDP, experience with high-scale consumer products. Recruitment Process: CV review – HR call – Interview – Client Interview – Decision