Python developer állások Németországban egyetemi végzettséggel

• 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:...

• 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

• 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.

• Build and run evaluation pipelines for LLM-based systems
• Analyze metrics, identify edge cases, and track quality
• Create and maintain Golden and Silver datasets
• Work with evaluation frameworks and tools (like Langfuse)
• Develop Python scripts for AI workflows (APIs, data processing, monitoring)

You operate, harden, and evolve our authoritative and recursive DNS, DHCP, forward-proxy, DNS and IP Management (DIM), and NTP services so they stay boringly reliable at internet scale...

• Design/build AI-ready Lakehouse architectures (Databricks, Fabric) and data pipelines for analytics/ML
• Develop financial data models (Data Vault 2.0) with governance (Purview, Unity Catalog)
• Architect AI infrastructure: Cognitive Search, RAG, vector DBs, knowledge graphs for agentic AI

As a Machine Learning Engineer in our Retail ML team, you will take end‑to‑end ownership of designing, developing and maintaining advanced machine learning solutions that directly influence our commercial performance. Your work will span dynamic pricing, demand forecasting and data‑driven buying...

• Design, maintain and deploy scalable, end-to-end training pipelines, including hyperparameter optimization
• Transfer control policies from simulation to real robotic hardware
• Integrate learned policies into a full-stack robotic system, including perception, planning, and actuation

Where you belong: Where you belong: ...

• Which structural and semantic gaps exist in the Celonis OCPM representation of the current Mercedes-Benz planning process?
• Which semantic concepts of our approach can be formalized as a Semantic Layer (e.g., as an ontology / knowledge graph)?

You design and develop next-generation architecture at the intersection of multi-modal perception, foundation models, behavior learning, and end-to-end autonomous driving....

• Investigate and resolve data inconsistencies across complex end-to-end data pipelines
• Automate complex analytics across large datasets to validate outputs and identify anomalies or inconsistencies
• Debug pipeline failures and improve overall pipeline robustness