[Hiring] MLOps Engineer @Poland and Central-Eastern Europe

Role Description

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.

Qualifications

• 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.

Requirements

• 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