Python developer állások Németországban (it tanácsadó, elemző, auditor)

Ownership of Data Assets & strategy: Own pre-training, evaluation, and meta-data including proprietary, open-source, and synthetic datasets. Develop and execute vision....

Own SEO data pipelines end-to-end. Design, build, and maintain automated pipelines that power all SEO analytics, from ingestion through transformation to dashboarding....

Lead, Plan, and Deliver: You will own the technical execution for the AI team. This includes translating high-level user stories into clearly defined technical work packages, creating robust delivery plans in close cooperation with the SW Engineering Team and your own team, and ensuring timely,...

• 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...

As a PhD candidate, your tasks will include investigating how foundation models can be effectively applied in industrial reality. To this end, you will develop innovative methods for adaptation, validation, and deployment....

• 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

Manipulating and analyzing complex, high-volume and high-dimensional data to gain insights by using appropriate data analysis techniques, algorithms and programming languages in close collaboration with subject matter experts...

• Developing a transformer-based neural network for the processing of NMR spectra
• Creating datasets from existing experiments within the CRC and from your own experiments, which are to be carried out during a research stay at KIT's Institute of Microstructure Technology (IMT)

• 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)?

• Translate R&D business and user requirements into effective system, process, and automation solutions
• Design, implement, test, and continuously improve automated workflows, AI-augmented data flows and AI Agents
• Ensure stable data flows, high data quality, and reliable system operations

Architect the ML Platform: Design, implement, and maintain a robust MLOps infrastructure that enables researchers to move seamlessly from local experimentation to global production....

• Contribute to pioneering technical advances to the field of foundation models on structured data, as well as exploring world models for solving business problems.
• Define the data, training, and experimentation environment to enable scalable access to data and compute.

Design, build, and maintain scalable data platforms and data & AI solutions tailored to business needs, including the development of governance structures and access control concepts...

• Drive pioneering technical advances to the field of foundation models on structured data, as well as exploring world models for solving business problems.
• Expand the horizon of what is possible by bringing together rich semantic context with large scale business data

• Define and evolve data models for R&D entities
• Design and implement robust data pipelines and automated data quality checks
• Improve traceability and metadata management so data can be understood, trusted, and reused across teams

In this PhD project, you will explore new approaches for creating the next generation of efficient autonomous decision-making systems....

• Designing and implementing end-to-end data flows from device data ingestion through transformation, storage and consumption
• Defining data models, structures and lifecycle concepts to ensure data quality, consistency and lineage