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9 дней назад

Senior Platform Engineer (AI)

Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
c1
Страна
France
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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TL;DR
Senior Platform Engineer (AI) (GCP/Kubernetes/GPU): Building and operating a GPU platform and self-service compute infrastructure for AI model training and serving with an accent on Kubernetes, infrastructure-as-code, GitOps, and high-performance workloads. Focus on designing reliable production clusters, automating deployment workflows, controlling cloud costs, and enabling data science teams to move from proof of concept to production.

Location: Paris, France; office-based

Company

A global gaming organization creating original interactive entertainment experiences across major studios and franchises.

What you will do

  • Design, build, and operate a GPU platform on GCP for AI workloads using infrastructure-as-code.
  • Deliver self-service compute for data science teams, including Ray on Kubernetes, job submission, queuing, quotas, and cost visibility.
  • Establish golden paths for deploying services so product teams can ship without performing recurring operations manually.
  • Support the deployment, hosting, and production operation of machine learning models.
  • Control infrastructure costs across GPUs, cloud resources, and related services.
  • Collaborate with data science teams from proof of concept through production deployment while improving system performance, reliability, and efficiency.

Requirements

  • Significant experience as a Platform, DevOps, or MLOps Engineer, with strong software engineering fundamentals and distributed systems knowledge.
  • Hands-on experience with Kubernetes, Terraform, GitOps, CRDs and operators, scheduling, autoscaling, node pool design, state management, ArgoCD, and Helm.
  • Solid experience with GCP; AWS experience is an advantage.
  • Platform-as-a-product mindset, autonomy, comfort with ambiguity, and ability to treat data scientists as platform users.
  • Fluent English, written and spoken, is required.

Nice to have

  • Production experience running or optimizing GPU-based workloads for machine learning training or inference.
  • Experience with distributed job scheduling such as KubeRay, Slurm, or Kubeflow.
  • Experience with cross-cloud networking between AWS and GCP.
  • Familiarity with MLOps tools such as MLflow, Weights & Biases, and model registries.

Culture & Benefits

  • Permanent full-time employment in an office-based environment.
  • Access to an internal e-learning platform from the first day.
  • Access to a game library, consoles, competitor games, and board games.
  • Works council discounts for entertainment, fitness, cultural activities, and leisure.
  • Career and development planning after one year, plus clubs, sports activities, and organized weekends.

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