Platforms engineer állások, munkák Németországban

• Design, implement and operate scalable .NET backend services from idea and implementation to deployment, monitoring, optimization and production follow-up.
• Break down work into small, shippable pieces, estimate effort and update estimates as scope evolves.

• Incident Management: Analyze, document, and resolve operational incidents (~20% of working time), including escalation and coordination within the team.
• Change Management: Plan, assess impact, coordinate, and implement changes.

Drive the adoption and effective use of AI solutions (e.g., Azure AI, Copilot) to optimize internal processes and enhance employee productivity...

• Design, build, and maintain scalable data platforms and solutions tailored to business needs
• Collaborate with stakeholders across teams to understand requirements and deliver impactful data initiatives
• Optimize and monitor data workflows to ensure performance, scalability, and reliability

Kubernetes orchestration: You will be responsible for setting up, operating and optimising our Kubernetes clusters using Rancher and AWS EKS in accordance with current best practices....

• Design, build, and maintain scalable data platforms and solutions tailored to business needs
• Collaborate with stakeholders across teams to understand requirements and deliver impactful data initiatives
• Optimize and monitor data workflows to ensure performance, scalability, and reliability

You will be a key driver in promoting and implementing innovative Artificial Intelligence solutions within Bosch. Your work will provide significant value to our global engineering quality organization....

You build and maintain end-to-end ML pipelines using workflow orchestration tools: from data ingestion to distributed training, evaluation, model compilation, and deployment-ready artefacts....

• Work closely with Product Management to understand the business needs and find appropriate technical solutions
• Design, own and operate scalable, secure payment systems that handle financial transactions efficiently in a multi‑tenant Cloud/SaaS environment

Architecture & System Design: Identify and address architectural limitations, make pragmatic trade-offs, and help evolve systems toward scalability, reliability, and long-term maintainability....

• Operate, maintain, and further develop cloud-managed Kubernetes clusters
• Build, maintain, and continuously improve CI/CD pipelines using GitLab CI
• Develop and maintain infrastructure as code using Terraform, Ansible, and Helm
• Automate operational tasks and improve developer workflows

• Architect and build the high-performance backbone of our data platform
• Focus on distributed computing stability and building effective caching strategies for large-scale operations
• Design ETL pipelines and architect resilient caching for complex dependency resolutions

• Operate and maintain complex Hyperscaler/SAP Cloud Infrastructure networks, track significant problems, monitor performance, and perform upgrades as required.
• Engage with customers in network architecture discussions to meet their security requirements.

• Operation and continuous improvement of SD-WAN and Office LAN infrastructure, including Wi-Fi and captive portal environments
• Continuous optimization and expansion of our Azure-based cloud network infrastructure

• Build Fullstack Features: Implement user-facing features using modern frameworks and connect them to backend APIs.
• Backend Development: Contribute to Python-based backend services (Django REST or FastAPI), including API endpoints and business logic.

• You join a stable team of experienced developers to maintain and extend our market leading application for aeronautical message handling.
• You contribute to all phases of the software life cycle - requirements engineering, design, implementation, verification, and maintenance.

• You design, build, and manage Kubernetes clusters on AWS EKS with high security and scalability standards for autonomous driving AI/ML workloads.
• Furthermore, you provision and orchestrate large-scale GPU clusters for distributed AI/ML training and experimentation.

• Develop and operate Java microservices that power Adobe's cloud platform, supporting a large global audience
• Improve the performance, reliability, and security of services your team owns end-to-end

• Develop and maintain ETL/ELT pipelines for both structured and unstructured data
• Ensure the integrity, availability, and performance of data across ingestion and storage layers
• Collaborate with analysts and architects to translate business requirements into scalable data models

You design the reference architecture for the ML platform end-to-end: data ingestion, PB-scale data lake, heterogeneous training clusters, model registry, and deployment-ready artefacts....