Freelancer profile translated to English.
Back to original languageAbout Oliver
Promoted Senior Data & AI Engineer with over ten years of experience in Data Engineering, Data Science, Cloud Platforms, Process Automation, and Production AI Systems.
I support companies in implementing data and AI use cases from analysis to production operation. I take care of architecture, data pipelines, backend, APIs, AI components, deployment, and integration into existing systems.
Focus Areas:
- Generative AI, LLM Applications, and RAG
- AI Agents, Agentic AI, and Intelligent Workflows
- Knowledge Assistants and Semantic Search
- Document Processing, Email Classification, and Information Extraction
- Process Automation with n8n, APIs, Webhooks, and Events
- ETL/ELT, Data Warehouses, Lakehouses, and Cloud Data Platforms
- Predictive Analytics, Machine Learning, NLP, and Reporting
- Local, Hybrid, and Sovereign AI Architectures
Technologies:
Python, SQL, FastAPI, PostgreSQL, Docker, n8n, Microsoft Azure, Azure Data Factory, Azure Synapse, Azure Databricks, Microsoft Fabric, AWS, Spark, Power BI, LangChain, LangGraph, OpenAI, Hugging Face, Ollama, vLLM, LiteLLM, pgvector, REST APIs, CI/CD, and Monitoring.
Typical Projects:
- Enterprise RAG for internal documents and corporate knowledge
- LLM-based document and email automation
- Agent systems for reporting, research, and process control
- Data platforms, analytics solutions, and predictive maintenance
- Integration of CRM, ERP, file, task, and knowledge systems
I work directly with business departments, product management, IT, and management. My focus is on functional, understandable, and maintainable solutions rather than pure concept papers.
Possible Roles: Senior Data Engineer, AI Engineer, Generative AI Engineer, LLM Engineer, Machine Learning Engineer, Data Scientist, Automation Consultant, or Solution Architect.
English
Fluent
German
Native or bilingual
Can work on-site
Saarbrücken (up to 50km), Frankfurt am Main (up to 50km), Berlin (up to 50km)
Experience
- Dr. Oliver Köhn – Data, Automation & AISenior Data & AI Engineer | Generative AI, Automation & Data PlatformsDIGITAL AND ITNovember 2024 - Today (1 year and 9 months)66 Saarbrücken, GermanyI develop production data, automation, and AI solutions from process analysis to operation. My focus is on Generative AI, LLMs, RAG, AI Agents, Intelligent Document Processing, Workflow Automation, System Integration, and modern data platforms.Selected Projects:
- LLM-based Email Processing: Classification of free-form messages, extraction of people, deadlines, references, and responsibilities, as well as automatic creation of tasks and follow-up processes.
- Document Automation: Analysis of incoming PDFs, structured data capture, and transfer to file storage, Kanban, knowledge systems, and team communication.
- Enterprise RAG: Knowledge assistant for contracts, policies, and internal documents with semantic search, source display, permissions, and flexible model routing.
- Agentic AI for Reporting: Merging multiple data sources, analysis of key figures, and automated creation of understandable management reports.
- Local and Hybrid AI: Operation of sensitive applications with open or interchangeable models on controlled infrastructure.
Technologies: Python, FastAPI, PostgreSQL, SQL, LangChain, LangGraph, OpenAI, Hugging Face, Ollama, vLLM, LiteLLM, pgvector, n8n, REST APIs, Webhooks, Docker, Azure, and AWS. - Public Cloud Group Holding GmbHSenior Data & AI EngineerDIGITAL AND ITAugust 2024 - November 2024 (3 months)66 Saarbrücken, GermanyDevelopment of production AI solutions and data-driven processes in cloud and enterprise environments. The focus was on scalable platform components, Generative AI, RAG, document analysis, predictive analytics, and the integration of machine learning and LLM applications into robust system architectures.Selected Projects:
- Generative AI Platform: Contribution to a central platform for AI-powered content and knowledge processes. Provision of reusable functions for model access, prompt management, structured outputs, RAG, and the connection of various data sources. Access control, scaling, monitoring, CI/CD, and cloud deployment were considered.
- Automated Document Analysis: Extraction, classification, and validation of relevant content from extensive, unstructured documents. Transfer of results into structured data for reporting and auditing processes with traceable assignment to the source documents.
- Predictive Analytics for Machine Data: Processing of operational, status, and sensor data from SAP-related and other sources for early detection of potential failures and support of maintenance planning.
Technologies: AWS, Microsoft Azure, Python, FastAPI, LLMs, RAG, Machine Learning, ETL, REST APIs, Docker, CI/CD, and Monitoring. - Adesso Schweiz AGSenior Data ConsultantDIGITAL AND ITJuly 2022 - July 2024 (2 years)Zürich, SwitzerlandConception and implementation of cloud data platforms, analytics solutions, and machine learning applications in the Microsoft Azure environment. Consulting for business departments and technical teams, as well as end-to-end realization of production data, analysis, and reporting processes.Selected Projects:
- Predictive Maintenance: Development of a complete processing chain for operational, sensor, and maintenance data. Implementation of data integration, data cleansing, feature engineering, model training, model evaluation, and deployment of predictions in analysis and reporting interfaces.
- Azure Analytics Platform: Development of automated ETL and ELT pipelines for merging multiple operational data sources. Building consistent data models for dashboards, self-service analytics, and data-based decisions.
- Cloud Migration and Reporting Modernization: Analysis and transfer of historically grown data, transformation, and reporting structures into an Azure-based architecture. Optimization of data models, queries, and Power BI reports.
- NLP and Sentiment Analysis: Automated classification and thematic analysis of customer feedback and free texts with visualization of results in Power BI.
Technologies: Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Python, SQL, Spark, ETL/ELT, Machine Learning, NLP, Power BI, Tableau, and CI/CD.
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Education
- Dr. rer. nat. in PhysicsSaarland University / University of Wisconsin–Madison2020Promotion in Physik mit Schwerpunkt auf statistischer Analyse, Zeitreihen, probabilistischer Modellierung und datengetriebener Auswertung komplexer Bewegungs- und Trajektoriendaten. Im Rahmen der Forschung entwickelte ich reproduzierbare Analyse- und Simulationsprozesse für große experimentelle Datenbestände. Die Arbeit umfasste Datenbereinigung, statistische Modellierung, Machine Learning, Mustererkennung, Visualisierung sowie den Vergleich experimenteller Daten mit mathematischen und physikalischen Modellen. Technisch arbeitete ich unter anderem mit Python, TensorFlow, C++, Bash und High Performance Computing. Die Promotion bildet die Grundlage für meine heutige Arbeit an komplexen Data-Science-, Predictive-Analytics- und KI-Anwendungen. Fähigkeiten Machine Learning, Data Science, Zeitreihenanalyse, statistische Modellierung, Python, TensorFlow, C++, High Performance Computing, Simulation, Pattern Recognition
- Master of Science (Physics)Saarland University2015Im Rahmen meines Masterstudiums in Physik habe ich mich auf die Schnittstelle zwischen theoretischer Physik, Machine Learning und quanteninspirierten Algorithmen spezialisiert. Meine Masterarbeit befasste sich mit der Anwendung von künstlicher Intelligenz (AI) auf physikalische Zwei-Niveau-Systeme – ein zentraler Bestandteil vieler Quantencomputing-Ansätze. Die Arbeit kombinierte klassische numerische Methoden mit modernen Machine-Learning-Verfahren, um Messprozesse für die Initialisierung von Quantencomputern zu optimieren. Mithilfe von Particle Swarm Optimization (Python, Bash) konnte die Qualität der Messdaten signifikant verbessert werden. Zusätzlich kamen sequentielle Monte-Carlo-Methoden (Fortran, Python) zum Einsatz, um Zwei-Niveau-Systeme präzise zu charakterisieren – unter Nutzung von HPC-Ressourcen (Cluster Computing). Kernkompetenzen und Technologien: - AI & Machine Learning in Quantum Physics - Optimization Algorithms: Particle Swarm Optimization - Bayesian Inference & Monte Carlo Methods - High Performance Computing / Cluster-Umgebungen - Programmierung in Python, Fortran, Bash - Anwendung datengetriebener Methoden auf quantendynamische Systeme Dieses Projekt war ein früher Schritt in Richtung interdisziplinärer Data Science, bei dem physikalisches Fachwissen durch den Einsatz intelligenter Algorithmen ergänzt wurde. Es legte die Basis für meine spätere Arbeit an der Schnittstelle von AI, Data Engineering und Forschung.
Certifications
- Microsoft Certified: Fabric Analytics Engineer AssociateMicrosoft2024
- Microsoft Certified: Azure Data Engineer AssociateMicrosoft2022