Platforms engineer állások Németországban (it programozás, fejlesztés)

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....

• 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

• 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.

• 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....

Directly impact your team's tech scope: work hand in hand with PM and EM to manage product technical roadmaps and dependencies to minimise technical debt....

Model Onboarding & Deployment: Take AI models from internal and external users into production inference workflows. Package and deploy models as containerized inference endpoints. Define and operate validation gates covering functional checks, performance benchmarks, and safety/​security...

Cloud platform engineering: Develop and implement automation processes for the deployment and operation of cloud software and services using platform engineering, infrastructure as code (e.g., Terraform), and Kubernetes....

NEW SOLUTIONS EMERGE WHEN THINKING ISN'T HARD-WIRED. More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly new solutions. Naturally, this includes flexible working models, so that our...

• Development and maintenance of embedded software components in C/C++
• Porting and rehosting existing software solutions to new target architectures
• Parallelisation of software processes on multi-core processors
• Integration and use of tools such as Tiger Lab for simulation and analysis

• Designing the overall system architecture for complex research platforms
• Conceptualizing data submission workflows for sensitive medical imaging data
• Defining architectural concepts for benchmarking and evaluation platforms, including secure execution of submitted models

• Build and evolve mobile solutions: Design, develop, and maintain high-quality React Native applications.
• Own architecture and technical decisions: Understand platform specifics, runtime behavior, dependencies, and deployment pipelines to ensure maintainable, efficient, and scalable mobile solutions.