Senior GPU Platform Engineer for AI on Kubernetes

Ubisoft; Paris; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Ubisoft Paris is seeking a Platform DevOps/MLOps Engineer to design and operate a GPU-focused platform on GCP for AI workloads. You will help Data Scientists and ML Engineers build and run AI services, and play a key role in creating a Kubernetes cluster, deploying self-service applications, and mentoring future hires.

You will drive cost-aware infrastructure, collaborate with cross-functional teams, and establish best practices for scalable, production-ready ML pipelines.

Qualifications

• Significant experience as Platform/DevOps/MLOps Engineer with strong software engineering fundamentals.
• Hands-on with Kubernetes, Terraform, and GitOps practices including CRDs, operators, scheduling and autoscaling, ArgoCD, and Helm.
• Solid experience with GCP; AWS exposure is a strong plus.
• Platform-as-a-product mindset; capable of autonomy and documenting solutions.

Responsabilités

• Design, build and operate a GPU platform on GCP for AI workloads, infrastructure-as-code.
• Deliver self-service compute for data science, e.g. Ray on Kubernetes, with job submission, queuing, quotas and cost visibility.
• Establish golden paths for deploying services so product teams can ship a new service without repetitive operations.
• Assist IT Squad to ensure deployment, hosting, and smooth operation of models in production.
• Oversee and control infrastructure costs, including GPUs, cloud resources, and related services.
• Collaborate closely with Data Science teams from PoC to production deployment.
• Continuously improve performance, reliability and efficiency of systems.
• Automate internal workflows using AI-powered systems where applicable.

Connaissances

Kubernetes

Distributed Systems

Software Engineering fundamentals

English fluent

Outils

Terraform

GitOps

ArgoCD

Helm

KubeRay

Kubeflow

GCP

AWS

Description du poste

Ubisoft Paris is seeking a Platform DevOps/MLOps Engineer to design and operate a GPU-focused platform on GCP for AI workloads. You will help Data Scientists and ML Engineers build and run AI services, and play a key role in creating a Kubernetes cluster, deploying self-service applications, and mentoring future hires.

You will drive cost-aware infrastructure, collaborate with cross-functional teams, and establish best practices for scalable, production-ready ML pipelines.