Senior Platform Engineer – Cloud & ML Platform (m/f/d)

<p>As a Platform Engineer – Cloud &amp; ML Platform (m/f/d), you will be a key contributor to the cloud-native infrastructure that powers our AI and autonomy development at global scale. You will design, deploy, operate, and continuously improve Kubernetes-based platforms that enable our teams to train, evaluate, deploy, and monitor machine learning workloads reliably across regions, clouds, and compute environments.</p><p>At Quantum Systems, we build intelligent unmanned systems that operate under real-world constraints. Our AI teams depend on scalable, secure, and high-performance infrastructure to turn data, models, and experiments into field-ready capabilities. In this role, you will help build the cloud and ML platform backbone that makes this possible.</p><p>You will work closely with AI engineers, data engineers, software teams, security, IT, and product stakeholders to provide robust, automated, and developer-friendly infrastructure for large-scale ML workloads. Your work will directly support our mission to push the boundaries of autonomous systems through cutting-edge software, edge computing, and real-time AI-powered data processing.</p><p style=";"></p><h4><strong>What is your Day to Day Mission:</strong></h4><ul><li><p>Design, deploy, operate, and continuously improve Kubernetes-based platforms for machine learning and data-intensive workloads.</p></li><li><p>Build and maintain globally distributed Kubernetes clusters with a strong focus on reliability, scalability, security, and observability.</p></li><li><p>Own the lifecycle management of ML platform components, including <strong>Kubeflow</strong>, <strong>Metaflow</strong>, workflow orchestration, experiment tracking, and related MLOps tooling.</p></li><li><p>Enable AI and data teams to run scalable training, inference, evaluation, and data processing pipelines across heterogeneous compute environments.</p></li><li><p>Develop infrastructure-as-code, automation, and GitOps workflows to ensure reproducible, auditable, and efficient platform operations.</p></li><li><p>Manage GPU-enabled workloads, scheduling, storage, networking, secrets, access control, and cost-aware resource utilization.</p></li><li><p>Improve platform resilience through monitoring, alerting, incident response, backup strategies, disaster recovery, and capacity planning.</p></li><li><p>Collaborate with AI, software, DevOps, security, and IT teams to define platform standards, best practices, and deployment patterns.</p></li><li><p>Support hybrid and multi-cloud infrastructure scenarios, including on-premise, private cloud, and public cloud environments.</p></li><li><p>Evaluate and integrate cloud providers and infrastructure technologies, including Azure, AWS, Telekom Cloud, or comparable platforms.</p></li><li><p>Continuously improve developer experience for ML engineers through self-service tooling, documentation, templates, and platform abstractions.</p></li><li><p>Help bring AI capabilities from prototype to p