Senior Platform Engineer - W/M/NB

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. Nice to Have 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. Joining us means having access from day one to: 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, and board games; Our works council (CSE) offering discounted rates on a wide range of shows, cinemas, concerts, gyms, amusement parks, and many other great deals. And after one year? You’ll have a career and development plan established with your manager, along with clear goals to help you grow each year; You’ll have joined one of our clubs: choir, yoga, tennis, lightsaber… or even created your own! You’ll enjoy your 1.5-hour lunch break by taking a class in our gym or borrowing one of our bikes for a ride in the Bois de Vincennes; You’ll be looking forward to the next surf/yoga or ski weekend organized by the CSE; You’ll have become unbeatable at foosball, climbing, Just Dance, or Boomerang Fu; You’ll have raided the internal sale to complete your collection of Ubisoft goodies and games (because you can never have too many). Ubisoft is an equal opportunity employer and offers all individuals the same access to employment, regardless of gender, ethnicity, religion, sexual orientation, social status, disability, or age. Ubisoft is committed to fostering an inclusive work environment that reflects the diversity of its player community.