04.09.2025 aktualisiert
100 % verfügbar
Senior Data Engineer / AI Specialist
Murnau, Deutschland
Deutschland
Computer Science (Informatik) DiplomaSkills
AI and machine learningPythonDjangoAmazon AWSLLMRAGBig DataSpark Software EngineeringData EngineeringGoogle GCP Data ScienceMicrosoft Azure
I'm a german Senior Data Engineer & Data Scientist with more than 15 years of professional experience.
I’m specialized in Big Data, Machine Learning / AI / NLP, Algorithms, GIS with technologies like Java, Spring, Python, Django, Spark and operating on all major cloud providers (AWS, Azure, GCP)
I have worked in large teams, also as a project manager, both technical and administrative, mostly using agile methodologies such as SCRUM, but also traditional "waterfall" processes.
Main sectors I have been working on are Automotive, (Renewable) Energy and Telecommunications.
I’m specialized in Big Data, Machine Learning / AI / NLP, Algorithms, GIS with technologies like Java, Spring, Python, Django, Spark and operating on all major cloud providers (AWS, Azure, GCP)
I have worked in large teams, also as a project manager, both technical and administrative, mostly using agile methodologies such as SCRUM, but also traditional "waterfall" processes.
Main sectors I have been working on are Automotive, (Renewable) Energy and Telecommunications.
Sprachen
DeutschMutterspracheEnglischverhandlungssicher
Projekthistorie
Right now I'm working on my own project developing AI agents controlling the browser resp websites using Stagehand (NodeJS / Typscript), Django and running on Google GCP via Terraform.
For different Web analytics use cases and sources I implemented a layered data architecture (Bronze, Silver, Gold “medaillon” architecture) which incrementally and progressively improves the structure and quality of data as it flows through each layer.
The final pipelines are deployed on Databricks (GCP), but the Python code is implemented and structured so it can run and be tested completely w/o Databricks e.g. with Github actions.
Delta tables are used so data versions can be rolled back. Updates based on data partitions are implemented, so data can be easily updated.
In another project I was working on Microservices inside a data streaming pipeline with Kafka and Python.
The final pipelines are deployed on Databricks (GCP), but the Python code is implemented and structured so it can run and be tested completely w/o Databricks e.g. with Github actions.
Delta tables are used so data versions can be rolled back. Updates based on data partitions are implemented, so data can be easily updated.
In another project I was working on Microservices inside a data streaming pipeline with Kafka and Python.
1. Finding optimal locations for new charging stations. Implemented data pipelines and Machine learning models for geospatial and socio-economic features and predicting the potential charging station usage on new locations. Python, Scikit Learn, Azure, Kubernetes, DBT and PostgreSQL.
2. Custom GPT: Combining internal data sources with ChatGPT by implementing a document management platform, and combining RAG with ChatGPT. Python, Django, Azure, OpenAI API, Azure AI Search/Indexing.
3. Knowledge management platform. Conceptual design of the software architecture and Azure infrastructure. Development of an API with Python and Django. Gitlab CI, Docker, Azure: App Services, Functions, Data Factory. Automation of infrastructure using Terraform. Supervising the multinational team as a senior developer.
4. Identification of optimal areas for self-supply communities (CSC) in Spain and Germany based on geospatial, socio-economic and environmental data (solar radiation). Based on my analysis the project Adeje Verde in Tenerife chose the central building for solar panels which provides renewable energy to its neighborhood
2. Custom GPT: Combining internal data sources with ChatGPT by implementing a document management platform, and combining RAG with ChatGPT. Python, Django, Azure, OpenAI API, Azure AI Search/Indexing.
3. Knowledge management platform. Conceptual design of the software architecture and Azure infrastructure. Development of an API with Python and Django. Gitlab CI, Docker, Azure: App Services, Functions, Data Factory. Automation of infrastructure using Terraform. Supervising the multinational team as a senior developer.
4. Identification of optimal areas for self-supply communities (CSC) in Spain and Germany based on geospatial, socio-economic and environmental data (solar radiation). Based on my analysis the project Adeje Verde in Tenerife chose the central building for solar panels which provides renewable energy to its neighborhood