Python developer állások Németországban egyetemi végzettséggel

• Express, purify and assemble multimeric protein complexes for structural studies (e.g., using the MultiBac and MultiMam systems)
• Biochemically characterize protein complexes, prepare, and image cryo-EM samples

Support Trading Activities: You support trading, operational, and analytical activities with a focus on emissions markets and contribute to commercial decision-making....

Design, develop, and maintain AI agents for manufacturing use cases (e.g. quality inspection, anomaly detection, process optimisation), using machine learning, deep learning, GenAI, rules, and reasoning....

Act as a key expert for SAP S/4HANA data migration projects, with particular focus on Material Master and Production Master (I.e., BOMs, Work Centers, Routings and Production Versions)....

You will take a key role in shaping and designing our edge platform for developing, testing, deploying, and operating real-time applications....

• Lead the technical lifecycle of ServiceNow and related governance platforms (IRM, Risk, Change, Incident, CMDB, Asset).
• Ensure configurations are secure, scalable, auditable, and aligned with regulatory standards.

Design, train, and evaluate machine learning models for manufacturing use cases such as quality prediction, anomaly detection, predictive maintenance, and process optimisation, covering the full ML lifecycle from problem definition to performance tuning....

Improve the reliability, scalability, performance, security, and cost-efficiency of the platform's microservices running on Kubernetes and AWS....

Develop next-generation discrete ESD and TVS protection devices for high-speed, RF sensitive and automotive applications....

• Own IAM strategy across Entra ID and Active Directory
• Manage conditional access, PIM/PAM, and identity lifecycle governance
• Develop identity-centric detections and threat-hunting workflows, analyzing Entra ID logs and privilege escalation paths in alignment with the SOC

• Build and maintain backend services and APIs using Java and Python
• Work on event-driven data pipelines based on Kafka
• Take ownership of services end-to-end: design, testing, deployment, monitoring, and iteration
• Ensure code quality, including writing and maintaining automated tests

Customer-Focused Outcomes: As a Senior SAP AI Engineer (f/m/d), you will be a key driver of customer value delivery, translating SAP AI architectural blueprints into fully functional, scalable, and production-ready solutions. Specifically, you will:...

You will analyse and evaluate technological developments and market trends in the areas of blockchain, DLT, smart contracts, tokenisation, CBDC and artificial intelligence....

• Lab validation measurements of semiconductor Silicon Carbide (SiC) Diodes and MOSFETs
• Define test strategies, test limits, and parameter setups for SiC MOSFETs, diodes, and power devices based on device specifications and characterization targets. Document test strategies in work instructions

• Developing and evaluating AI systems based on Large Language Models (LLMs), Vision-Language Models (VLMs), and multimodal foundation models
• Designing agentic workflows that automate complex image analysis tasks

• Build Data Pipelines: Design, build, and maintain scalable data pipelines for ingesting, transforming, and serving financial and operational data.
• Work with ERP Data: Integrate and model data from ERP systems and finance tools, including accounting, controlling, procurement, revenue, and cost data.

• Work closely with quantitative datasets: cleaning, processing, running frequencies, building analysis files and identifying patterns that matter
• Prepare chart output and visualisations for client-facing presentations, translating analytical findings into clear, structured stories

Analysis of the data ecosystem: assessment of data systems, interfaces and data quality; evaluation of security / privacy and interoperability (data-level to user-level)...

• Keep data moving: Build, run, and refine data pipelines and workflows (Python, Airflow/Cloud Composer) so they're reliable, fast, and easy to change
• Shape our data models: Use BigQueryand Cloud Spanner to design wellstructured, highperformance datasets for analytics and machinelearning use cases

• You understand RABOT's departments, their core processes and our AI tooling (Claude, Claude Code, our data stack) well enough to run a process-discovery session on your own.
• You've shipped your first automation into production — something a team feels immediately.