MLOps Engineer

Poland and Central-Eastern Europe; Polska; Remote; PLN 240,000 - 360,000; Full time

Job summary

Xebia in Europe is seeking an experienced MLOps Platform Engineer to own the company's machine learning platform infrastructure. You will manage Vertex AI, Kubeflow Pipelines, and GKE-based training and serving, building templates, CI/CD, and self-service workflows that let data scientists ship models without tickets.

You will oversee model lifecycle, feature/data flows with BigQuery and Airflow, implement GitOps, and ensure security and cost efficiency.

Qualifications

• 5+ years in MLOps or ML platform engineering.
• 2+ years owning production ML infrastructure end-to-end.
• Hands-on with GCP: Vertex AI, GKE, BigQuery.
• Strong Kubernetes expertise; can run and debug clusters.
• Experience with Kubeflow Pipelines or Vertex AI Pipelines and Airflow.
• GitOps for infra and CI/CD for model deployment.
• Production-grade Python, testing, packaging.
• Platform mindset with self-service tooling and SLOs.
• Ability to own a company-wide platform independently.

Responsibilities

• Own the machine learning platform infrastructure as part of the Data Infrastructure team.
• Manage Vertex AI, Kubeflow Pipelines, and GKE-based training/serving infra.
• Build templates, CI/CD, and self-service workflows for model shipping.
• Oversee model registry, versioning, deployment, and monitoring for drift.
• Support feature and training data flows across BigQuery and Airflow.
• Apply GitOps with Config Sync and enforce least-privilege access.
• Manage GPU and compute usage, quotas, and cost visibility.
• Own end-to-end platform used by vertical teams to build/train/serve models.

Skills

MLOps

Production ML infra

GCP Vertex AI

GKE

Kubernetes

Kubeflow Pipelines

Airflow

GitOps

CI/CD

Python

Self-service tooling

SLOs

Tools

Vertex AI

GKE

BigQuery

Kubernetes

Airflow

Kubeflow Pipelines

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:

• 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