Retrieval-Augmented Generation Experts in Munich

• Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
• Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
• Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.

• AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
• Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
• Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
• Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.

Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps

• Overall responsibility for the project lifecycle from scope definition to completion
• Close collaboration with platform teams, IT leaders, and external service providers
• Application of SAFe principles and structured sprint work
• Creation of a migration roadmap with clear milestones
• Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
• Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
• Regular status reports and running knowledge transfer sessions

• Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
• Used multiple experts (OpenAI models) collaborating during document drafting
• Extracted useful information from the knowledge graph
• Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
• Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
• Deployed initial application as a Streamlit app

• Ongoing independent executive practice for ERP/MES, digital manufacturing, IT/OT and program delivery in industrial and regulated environments.
  
  

• Lead transformation and recovery programs across Europe, the US and Asia across programs with total value exceeding EUR 180M. Focus areas include governance reset, multi-site execution, vendor control, ERP/MES target architecture, executive steering and transition to stable operations.
  
  

• Selected current and recent work:**
  
  

• PE-backed Industrial Carve-out, Europe | Transformation & Separation Advisor | February 2026 – Present Supporting carve-out preparation and execution with focus on separation logic, operating model, governance, TSA implications, IT/ERP dependencies and Day-1 readiness. Translating transaction perimeter into executable workstreams across business, IT, operations and finance, with clear decision cadence and risk transparency.
  
  

• Industrial Software & Automation, Europe | Industry Strategy & Value Proposition Advisory | Jul 2025 – Apr 2026 Shaped industry strategy, value proposition and executive positioning for focused industrial software and automation providers in adjacent but distinct markets, including warehouse automation, enterprise architecture, process intelligence and manufacturing transformation. Work included ICP clarity, use-case framing, GTM logic, customer value realization and the translation of ERP/MES, IT/OT and shopfloor reality into scalable, provider-specific market narratives.
  
  

• AI LegalTech Platform, Switzerland | Interim CTO | Sep 2024 – Jun 2025 Defined product, data and technology roadmap for an AI-enabled LegalTech platform under DACH regulatory constraints. Implemented RAG architecture and delivered MVP, pilot clients and partner setup for first revenues and investor readiness.
  
  

• Industrial Components, Switzerland | Manufacturing Transformation Program Lead | Jan 2023 – Dec 2024 Re-established governance and roadmap for an eight-year stalled MES replacement across multiple plants. Aligned ERP and Quality interfaces, restored delivery cadence and improved OEE by more than 15% in a CHF 550M business unit.
  
  

• Pharma & Chemicals, Ireland | GxP Manufacturing Systems Program Lead | May 2020 – Dec 2022 Took over a failing MES/DNC rollout in Covid-19 vaccine manufacturing under strict GxP constraints. Reset governance, validation and site support, restoring operational stability and Day-1 compliance.
  
  

• Premium Automotive, Italy | Paint Shop Digitalization Program Manager | Sep 2020 – Dec 2021 Stabilized a failed MES rollout that had reduced throughput to 8–9 cars per day versus 23 contracted. Increased output to 26–28 cars per day, securing continuity of a premium product line worth more than EUR 1B.
  
  

• Automotive Tier-1, Germany | Manufacturing Transformation Program Manager | Oct 2019 – Jun 2020 Modernized an unstable legacy MES blocking ERP integration and traceability. Defined target architecture, aligned vendors and plants, and enabled more than 20% OT operating cost reduction.

struppilog.com – Digital health record for pets / MVP → Full Product

Design, development, and full further development of a digital health platform for pets – from my own MVP development to a fully built and production-ready platform.

Independent concept and development of the MVP Development of the full application with Flutter/Dart and Firebase Expansion of the MVP into a full digital health record with health data, findings, allergies, medications, documents, and emergency data Development of user registration, authentication, roles, data models, and secure user interactions Implementation of QR-code-based data exchange and digital interaction features Development of a multilingual, responsive web application Integration of AI-supported features and AI/agentic workflows Development and continuous improvement of product logic, UX/UI, and technical architecture Building and expanding a scalable cloud-based solution with Firebase Integration and further development of APIs and external services Use of AI-native / agentic engineering to speed up development, testing, debugging, and product iteration Independent implementation of all other features and technical extensions Continuous further development of the MVP into a full digital product

Impact: The MVP I built myself was continuously developed technically and functionally into a broad, production-ready platform – including frontend, backend, data model, authentication, UX/UI, APIs, cloud infrastructure, and ongoing product development.

• Partner directly with C-level leadership (CEO, CAIO, CTO) on architecture, OKR strategy, and cross-team roadmap prioritization, translating strategic goals into structured engineering requirements.
• Surfaced and mapped technical debt across the entire organization with C-level leadership and co-defined a prioritized remediation strategy, balancing debt paydown against feature delivery.
• Led code reviews and technical standards across the team, fostering a mentor-first environment with two-way feedback dialogue — pairing on complex pipeline work and unblocking junior engineers on async architecture patterns.
• Re-architected the AI companion's core processing pipeline from synchronous to asynchronous with a queue-based worker architecture, enabling horizontal scalability and cutting upload processing time ~4x (from ~22s to 5–10s) while improving response accuracy.
• Designed an AI-driven document intelligence workflow with automatic multi-document classification, per-document summarization, and relevance guardrails for the patient care journey.
• Built a unified patient memory system (short- and long-term context) bridging the document vault and chatbot into a single bidirectional, context-aware platform.

• Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
• Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
• Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
• Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
• Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).

• Delivered backend systems, data processing solutions, and software integrations for enterprise business applications.
• Designed and implemented API-based services connecting internal platforms with external systems.
• Built automated processing workflows to handle large-scale structured business data.
• Improved application performance by 30–50% through database optimization, caching strategies, and backend refactoring.
• Reduced manual operational effort by 40–60% by automating repetitive workflows.
• Supported production environments through troubleshooting, monitoring improvements, and continuous optimization.
• Authored technical documentation and led knowledge-transfer sessions to support long-term maintainability.

AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.

• Completed an intensive AI Engineering Program focused on LLM evaluation, retrieval, agent orchestration, and multimodal workflows.
• Designed retrieval pipelines with document ingestion, semantic search, and citation-aware outputs using LangChain and ChromaDB.
• Built LangGraph-based agent workflows with state handling, clarification loops, and human-in-the-loop approval steps.
• Applied prompt engineering and evaluation patterns, including scoring logic and guardrails, to improve output quality and reliability.
• Worked extensively with Python, FastAPI, OpenAI APIs, LangChain, LangGraph, and ChromaDB in end-to-end implementations.

• Architected and built a GenAI-based automated asset-generation tool for social media campaigns using Nano Banana and Python. It takes a campaign brief, target audience, and two products as input, generates optimized prompts for image and text creation, and uses functions for text positioning, visually appealing overlays, resizing, and structured uploads to AWS S3.
• Engineered and built a multi-agent news intelligence platform with specialized roles including retriever agents (Tavily web scraping), synthesizer agents, and Claude as curator/orchestrator, designing autonomous agent collaboration patterns using LangChain and RAG.
• Built an autonomous customer service agent using n8n and LLMs, delivering end-to-end support automation with transparent reasoning, governance controls, and scalable workflow orchestration using Python and vector databases.
• Developed a financial validation engine featuring ML-powered anomaly detection for invoice plausibility, compliance automation, and risk mitigation using TensorFlow and SQL.
• Created a cost optimization application using OCR, AI, Pandas, and NumPy for data analysis to identify cost optimization potential.

• Clarification of project goals, scope, and functional target vision for building the AI-based knowledge base.
• Deriving the initial architecture and implementation strategy for the Zasterbot chatbot, including defining the MVP and expansion phases.
• Developing a functional target vision for building a structured knowledge base and integrating a future chatbot.
• Deriving and prioritizing use cases for information retrieval and provision by the chatbot.
• Modeling data structures and flows for effectively organizing the knowledge base on the Base44 platform.
• Designing and implementing data models for storing and linking relevant information.
• Developing processes for extracting, analyzing, and preparing raw data for the knowledge base.
• Ensuring data consistency and quality as the foundation for the future chatbot.
• Planning the integration of large language models (LLMs) and retrieval-augmented generation (RAG) for precise and context-aware responses.
• Implementing features for analyzing and visualizing data from the knowledge base.
• Using the Base44 platform with JSON-schema-based entities and a flexible permission model.
• Implementing Deno functions for backend logic, event processing, and external API integration.
• Integrating OpenAI services for initial data analysis.

• Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
• Designed and implemented multi-agent AI workflows for financial and trading applications
• Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
• Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
• Led system architecture decisions across model selection, orchestration, state management, and deployment