Optical Character Recognition Experts in Berlin

• Designed and developed AI-powered document processing solutions using Python, OCR, NLP, and Large Language Models (LLMs) to automate extraction, validation, and classification of financial documents, reducing processing time by 75%.
• Built intelligent multi-stage workflow automation pipelines integrating AI services, machine learning models, and enterprise systems to streamline financial operations and improve data quality.
• Developed reusable AI-driven transformation frameworks capable of processing structured and unstructured document formats (XML, CSV, JSON, TXT, DAT) and normalizing them into unified business schemas.
• Designed and developed Python-based REST APIs and backend services supporting enterprise finance applications and high-volume data processing workloads.
• Built scalable data synchronization pipelines between Oracle CFIN and SQL databases, incorporating machine learning models for cash-flow forecasting and AP/AR anomaly detection.
• Architected and deployed Apache Airflow workflows to orchestrate AI-powered data pipelines, automating end-to-end processing from document ingestion through financial system integration.
• Led the migration of critical enterprise integrations from MuleSoft to Python-based services, improving maintainability, performance, and operational flexibility while preserving complete data integrity.
• Managed the full API lifecycle including solution design, implementation, documentation, deployment, monitoring, and production support for mission-critical financial systems.
• Collaborated directly with finance stakeholders to identify business challenges, define solution requirements, and deliver measurable operational improvements through automation and AI-driven workflows.
• Worked closely with cross-functional engineering and business teams to rapidly iterate on features, improve processes, and drive successful adoption of AI-enabled solutions.
• Provided technical leadership through architecture reviews, technology decisions, code reviews, and engineering best practices across integration and automation initiatives.
• Mentored developers, established coding standards, and contributed to improving software quality, maintainability, and delivery effectiveness across projects.
• Provided production support during critical month-end and quarter-close financial processes, performing root-cause analysis and implementing rapid fixes to ensure system reliability and data accuracy.

• Insurance service provider*

Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.

Implementation:

• Architecture and production implementation of an on-premise OCR solution with full data ownership
• Methods for recognizing document structures as the basis for automated further processing
• ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations

Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year

Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL

• Responsibility for time, budget and quality targets of the sub-project
• Creation and maintenance of the sub-project plan including dependencies to FI, CO, PP, SD
• Definition of work packages, resource planning and progress monitoring
• Risk and change management within module scope
• Analysis of as-is processes in procurement, logistics and warehouse management
• Design of to-be processes according to SAP standard (EWM warehouse processes such as goods receipt, put-away, picking, goods issue)
• Coordination with key users and specialist departments
• Support for interface development with external warehouse systems and supplier portals
• Organization of test data, error tracking and test documentation
• Support for deployment preparation and key user training
• Participation in the cutover plan (data migration, go-live preparation)
• Support of the department during the hypercare phase after go-live
• Assistance in mapping EWM core processes
• Management of external service providers
• Use of Jira, Confluence, Signavio, MS Teams, Aqua ALM

• Overhaul of the entire design and implementation of new UI/UX aspects using React
• Planning of the software architecture and structuring of the project for long-term scalability using Git for version control

• Built a JTBD-based prioritization framework for 3,000+ accumulated feature requests, identified 27 broker jobs, validated 8 through 25 user interviews, and used the resulting job map as a live prioritization filter for all incoming channels (Upvoty, CSAT, consulting tickets).
• Responsible for the Scout24 Lighthouse initiative: Document Intelligence with full RAG architecture (semantic chunking, bge-m3 embeddings, pgvector, BM25+Dense hybrid retrieval).
• Reduced lead time of customer feature requests to 3.1 days through code analysis, ticket specification, and independent implementation using a coding agent (Codex).
• Developed an LLM-based support agent (GPT-4o mini, Codex-generated merge requests) that reduced 3rd-level escalations from 40% to 5% of all monthly tickets.
• Integrated six partners through technical coordination, specification, backlog and release management, and led seven full stack developers.
• Eliminated regulatory exposure for brokers in six weeks through risk analysis (BGH ruling on distance selling/GDPR), new audit features, and coordination with legal and data protection officers.

• Design of an LLM-based agent system to control the ERP software
• Development of agent workflows with LangGraph and PydanticAI
• Planning interfaces between business logic and language models
• Planning agent orchestration
• Prototype development and demonstration

Tools: Python, Pydantic, React, LangChain, LangGraph, Linux

• Designed and developed image classification models for computer vision tasks, including dataset creation, preprocessing, validation, and visualization; applied machine learning and deep learning techniques to optimize accuracy and performance.
• Conducted experiments with appropriate ML algorithms, LLM-based approaches, and tools; performed statistical analysis, hyperparameter tuning, and fine-tuning using test results for robust model performance.
• Developed and optimized CRUD operation APIs using Node.js (Fastify) and Flask (Python), improving latency and ensuring scalability.
• Implemented backend solutions with SQLAlchemy for database management, and automated workflows using cron jobs and AWS Lambda functions.
• Experienced in implementing and maintaining CI/CD pipelines to streamline deployments and ensure reliable software delivery.
• Consistently achieved service time and quality targets while maintaining strong, collaborative, and professional working relationships across teams.

• Leading the Demand & Inventory team, ensuring optimal stock levels and demand forecasting across European markets
• Developed forecasting models and optimized replenishment processes, reducing out-of-stock rates and improving stock turnover
• Prepared monthly stock and inventory reports for the Controlling department, including variance analyses and performance insights, providing the basis for financial steering
• Prepared executive-level PowerPoint presentations for board meetings, translating planning data into clear, decision-oriented insights
• Reported directly to the Board of Corporate Brands
• Collaborated cross-functionally with Marketing, Product, and Supply Chain teams to align forecasts with campaign calendars and new product launches
• Drove process improvements and reporting enhancements, increasing forecast accuracy and planning transparency across key stakeholders
• Initiated and led the drive-to-SharePoint integration test case within the English Demand & Inventory team, completed in just 7 days; adopted by the Digital department as a best practice to roll out across all Corporate Brands
• Provided support to the PwC team on a strategy & corporate communication restructuring project
• Successfully implemented a Net Sales Boost Plan across 13 countries within two months, achieving significant revenue growth while simultaneously reducing overstock levels

• Strategic portfolio management: Own the product vision and multi-year strategic roadmap for the global InventoryAutomation portfolio. Managed the integration of AI-powered efficiency tools across multiple operating entities, ensuring alignment with complex regulatory and governance standards.
• AI/ML impact at scale: Spearheaded mission-critical AI initiatives that automated 70% of global inventory rules. Leveraged predictive modeling to reduce manual errors by 40%, resulting in multi-million euro annual operational savings.
• Operational optimization: Defined and tracked high-level success metrics (e.g., CPO). Successfully achieved a 10% reduction in Cost Per Order (CPO) through the deployment of Generative AI assistants and automated financial workflow models.
• Executive stakeholder management: Acted as the primary liaison between Product, Engineering, Risk, Compliance, and Finance. Successfully navigated high-stakes negotiations to align disparate global entities on standardized payment protocols.
• Team mentorship: Led a cross-functional squad of Data Scientists, Engineers, and Designers, fostering a culture of rapid experimentation and "Responsible AI" deployment.

• Lecturer for the 'Digitalization in Business' course in the dual study program for insurance companies

• Defined the headless Checkout product strategy (APIs, contracts, extensibility) and roadmap; partnered with Engineering/SecOps to refactor and harden the platform for externalization.
• Aligned MDs/C-level on a multi-quarter roadmap, business cases, and success metrics (conversion, time-to-launch for partners, incident rate, SLA adherence).
• Chaired cross-functional planning (multiple PMs and squads); ran RICE prioritization and capacity planning for the unit; sequenced dependencies across Frontend, Payments, Platform, and Security.
• Rolled out a multi-tenant OIDC Identity Provider (SSO, RBAC, tenant isolation, data reuse) and integrated it with Checkout and partner apps; reduced login friction and support escalations.
• Established a governed Rules Engine (eligibility, promotions, shipping and payment rules, compliance guards) with versioning, safe rollouts, and clear ownership across teams.

• Designed and implemented end-to-end automated workflows for extracting structured data from semi-structured PDF documents including invoices and medical reports
• Leveraged Optical Character Recognition (OCR) technology and large language models to parse documents and generate validated JSON schemas
• Engineered prompt optimization strategies and rule-based classification hierarchies to enhance parsing accuracy across diverse document layouts
• Established quality assurance framework using evaluation metrics to validate output against ground truth datasets with 96% accuracy