Machine Learning Experts in Essen

• Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
• Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
• Development of robust REST APIs for automated document processing and system integration
• Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
• Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
• Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
• Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes

Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation

• Training the AI models, evaluating images and texts for UI/UX, turning the provided data into insights via OpenAI Feather as part of the machine learning workflow

Technologies: OpenAI Feather

Tools: Fabric, AWS, dbt, Power BI, SQL, DWH, R, Python

• Supported customers in implementing an architecture design for extracting and preparing data
• Planned the design and implementation of the BI and DWH platform
• Ensured the scalability and performance of the data platform

• Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
• Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.

Conventional Paradigm Test (CPT) – AI Evaluation & LLM Research

Development of an experimental evaluation approach to examine “paradigmatic closure” in Large Language Models — that is, the question of how far LLMs can recognize the basic assumptions, values, and limits of the paradigms within which they generate answers.

Design and testing of an additional approach to classic AI benchmarks that does not primarily measure factual correctness or task performance, but instead examines a model’s ability to recognize alternative perspectives, make implicit assumptions visible, and reflect on the limits of its own answer or interpretation framework.

Focus areas: development of evaluation criteria and test questions · LLM evaluation and comparative model analysis · prompt and response analysis · qualitative classification of model answers · study of epistemic compression and value leakage · benchmark and literature research · development of structured assessment and analysis methods

As part of CPT, existing AI evaluation approaches and benchmarks were analyzed, and a minimalist test protocol was developed that classifies model answers by response patterns such as DIRECT, CLARIFY, PLURALIST, REFUSE, and META-AWARE. TruthfulQA was used as the basis for experimental application and comparison with existing reference answers.

Technologies & Methods: Large Language Models (LLMs) · Generative AI · Prompt Engineering · AI Evaluation · TruthfulQA · Benchmark Analysis · Human-in-the-Loop Evaluation · Qualitative Content Analysis · Research & Literature Review

• Patent application: European patent drafting and prosecution
• LLM practicing: Developed LLM-based tools for automated patent data retrieval, applying Python scripting to accelerate technical reviews.

• Conducted advanced machine learning methods on large-scale inequality datasets for forecasting, clustering and feature importance to assess impacts on growth
• Integrated multi-source inequality datasets into a unified panel; applied reproducible preprocessing pipelines including variable harmonization, outlier treatment, normalization, and multiple-imputation methods
• Published four papers in international peer-reviewed journals

• Manage WordPress websites, ensuring SEO-friendly structures and high-performance functionality
• Create and optimize Google Ads campaigns, leveraging data to enhance conversion rates and ROI
• Develop Looker Studio & Power BI dashboards to track customer behavior, sales trends, and digital performance
• Implement Google Tag Manager (GTM) and Google Analytics (GA4) to enable precise event tracking and reporting

Conception, planning, and production deployment of two AI platforms for industrial research and engineering workflows, from use-case identification and requirements analysis through architecture decisions and build-vs-buy trade-offs to go-live.

• awRAG.io: Identification of the use case (fragmented knowledge base across distributed AI tools), definition of data requirements, architecture decision for a multi-tenant RAG-as-a-service platform with GDPR-compliant EU infrastructure and production-grade retrieval pipeline

• LAIERS.ai: Use-case definition (context loss in linear AI workflows), strategic product decisions on UX, cost structure, and multi-LLM orchestration, rollout of a spatial AI conversation platform with proprietary context management system LAICS

• LLMOps ownership: Quality assurance, pipeline optimization, security architecture (OAuth 2.0, SOC 2), and performance monitoring of both platforms in live production

• Core topics: LLM, RAG, vector databases, LLMOps, AI architecture strategy, cloud infrastructure, data sovereignty

• Expansion and support of the product portfolio for video analytics and computer vision
• Requirements gathering and engineering
• Consulting, project management and coordination of the pilot and international rollout
• Tools: IP-based camera and video systems, Citrix administration, OneNote, Microsoft Teams, Node-RED, Office 365, Age Verification, Qognify Umbrella, ThingsBoard, Xovis 3D sensors, AXIS cameras, GK checkout software, Atlassian JIRA

• Introduction, discussion, and hands-on session for a client in the staffing industry

• Creating the functional specification for a dashboard solution for E2E management in the 5G deployment team in an Open RAN architecture
• Developing dashboards and reports with Power BI Desktop (data connection using Power Query, KPI calculations with DAX, publishing to Power BI Service)
• Developing solutions with Power Apps
• Integrating Power BI reports into Dynamics 365 Business Central

Implemented Power BI reporting for various departments at an energy provider. Agile project: responsible for organizing and coordinating with departments and IT, and regularly presented interim steps and results to project management.

• Data preparation and analysis of user behavior for targeted marketing strategies
• Development and implementation of machine learning models, B2C customer segmentation and cluster analysis with Python
• Use of openpyxl and pandas for efficient automation, cleaning and standardization of datasets
• Close collaboration with interdisciplinary teams to integrate data-driven insights; result: +15% increase in marketing efficiency through improved customer retention
• Technologies & Tools: Python (Pandas, NumPy, scikit-learn), SQL (SQLAlchemy), Jupyter Notebook, creation of analysis and result reports (PDF, Word, Excel)