MLOps Engineer

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Senior0Full-time

#465527·Dodano 13 dni temu·2

Źródło: Xebia

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Tech Stack / Keywords

GCPVertex AIGKEKubeflow PipelinesBigQuery Airflow KubernetesGitOpsPythonConfig Sync

Podsumowanie oferty

Own the company-wide GCP data and ML platform as part of the Data Infrastructure team. Maintain its Vertex AI, Kubeflow Pipelines and GKE training and serving infrastructure, and build workflows that help data scientists deploy models.

Wymagania

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.
Experience building documented, self-service tooling for other teams, with SLOs.
Ability to work independently as the single owner of a company-wide platform.

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.

Obowiązki

Own the machine learning platform infrastructure as part of the Data Infrastructure team, which operates the company-wide GCP data and ML platform.
Own Vertex AI, Kubeflow Pipelines and GKE-based training and serving infrastructure, keeping it reliable, secure and cost-efficient.
Build templates, CI/CD and self-service workflows that enable data scientists to ship models without filing tickets.
Manage the model lifecycle, including model registry, versioning, deployment, rollback, and monitoring for drift and serving health.
Support feature and training data flows across BigQuery and Airflow, in collaboration with data platform engineers.
Manage infrastructure through GitOps with Config Sync, applying least-privilege access for PII and policy-tagged data.
Manage GPU and compute usage, quotas, and per-team spend visibility.
Take end-to-end ownership of the platform that vertical teams use to build, train and serve their models.

Inne informacje

Work from the European Union region and a work permit are required.
Recruitment process: CV review, HR call, interview, client interview, decision.

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