Senior Platform Engineer

Company Description
Ubisoft is a global leader in gaming with teams across the world creating original and memorable gaming experiences, from Assassin’s Creed, Rainbow Six to Just Dance and more. We believe diverse perspectives help both players and teams thrive. If you’re passionate about innovation and pushing entertainment boundaries, join our journey and help us create the unknown

Company Description
Ubisoft is a global leader in gaming with teams across the world creating original and memorable gaming experiences, from Assassin’s Creed, Rainbow Six to Just Dance and more. We believe diverse perspectives help both players and teams thrive. If you’re passionate about innovation and pushing entertainment boundaries, join our journey and help us create the unknown
Ubisoft's pioneering studio, Paris studio was responsible for the publisher's first successes following its creation in 1992. Today, it is at the helm of such must-have licenses as Just Dance, Ghost Recon and Mario + The Rabbids. Ubisoft Paris has also built a solid reputation as a partner of choice in cross-studio collaborations such as Watch Dogs, Skull & Bones, Beyond Good & Evil 2 and Star Wars. Building on this experience, the studio continues to push the boundaries of creativity by working on some very promising yet unannounced projects.
With 750 talents of 35 nationalities, Ubisoft Paris is today the largest studio in France and one of the most experienced in the industry. Young talents can benefit from the strong presence of senior profiles in the creative, technical or artistic fields. Everyone is driven by the same passion: to push the limits of what is possible and to offer new experiences to our players.
Ready to join the adventure? Join us at the Paris Studio

Job Description
You will be the first member of the new Platform Team, focused on GPU workloads and high-performance model serving & training technologies. You will bring your DevOps expertise to help Data Scientists and Machine Learning Engineers build and operate AI services. You will play a key role in building and operating a new Kubernetes cluster, deploying self-service applications for other teams, establishing best practices and new workflows and mentoring future hires. In this capacity, you will:

• Design, build and operate a GPU platform on GCP for AI workloads, fully expressed as infrastructure-as-code
• Deliver self-service compute for the data science team, 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 having to perform operations each time.
• Assist the existing IT Squad in ensuring the deployment, hosting, and smooth operation of models in production environments.
• Oversee and control infrastructure costs, including GPUs, cloud resources, and related services.
• Collaborate closely with Data Science teams, assisting them from the PoC stage to service deployment on production clusters.
• Continuously improve the performance, reliability, and efficiency of above systems.
• Automate internal workflows, utilizing AI-powered systems wherever applicable.
  

• You have significant experience as a Platform, DevOps, or MLOps Engineer, with strong Software Engineering fundamentals and a good understanding of Distributed Systems.
• You have strong hands-on experience with Kubernetes, Terraform, and GitOps practices, including CRDs and operators, scheduling and autoscaling, node pool design, state management, ArgoCD, and Helm.
• You have solid experience with GCP; real-world exposure to AWS is a strong plus.
• You have a platform-as-a-product mindset and are comfortable working with ambiguity and autonomy: you treat data scientists as users, document your solutions, measure adoption, research solutions independently, and set technical precedents when needed.
• You are fluent in English, both written and spoken, and can communicate effectively with a wide range of stakeholders.
  

• Hands-on experience running or optimizing GPU-based workloads in production, ideally for ML training or inference.
• Experience with distributed job scheduling, such as KubeRay, Slurm, or Kubeflow.
• Experience with cross-cloud networking, including Interconnect, VPN, Private Service Connect, and identity federation between AWS and GCP.
• Familiarity with MLOps tooling, such as MLflow, Weights & Biases, and model registries.
  

• Our internal e-learning platform to finally train on the tools you’ve always wanted to master;
• Our game library where you can borrow the latest Ubisoft titles, competitor games, consoles,