GPT Experts in Munich

Testing and verification of high-voltage systems

• Performed integration and system tests for control units in PHEV/EV vehicles using ECU-TEST (TraceTronic), Vector CANoe, CANalyzer, ETAS INCA, Tornado, E-Sys, and EDIABAS.
• Analyzed the interaction of high-voltage control units (including CCU, BMU, inverter, IPB, and IPF) and carried out software updates and flash processes to verify new software versions.
• Worked closely with software, system, and integration teams in an agile development environment to analyze issues and verify new software versions.

• AI-native B2B SaaS for freelancer sourcing; DACH market.*

• Enterprise partnerships across four industries: structured and closed multi-stakeholder deals with Telefónica (Telco), Emma Matratzen (Retail), Nürnberger Versicherungen and Flatex (Financial Services), Hubert Burda Media and Serviceplan Gruppe (Media).

• Revenue and growth: scaled FRATCH from €0 to €3.8M annual GMV, with ~80% of revenue sourced from founder-led direct outreach and partner relationships.

• Channel partnerships: sold FRATCH as a SaaS solution to recruiting firms (e.g., YER) — built the partner-enabled motion alongside direct enterprise sales.

• Team build: scaled FRATCH from solo founder to a team of 7 across engineering, product design, operations, and supply outreach.

• Proprietary network asset: onboarded 15,000+ freelancers as registered users — the proprietary DACH network powering FRATCH's matching.

• Built and launched FRATCH GPT (fratch.io/gpt): a production conversational AI agent. Architected the full stack — LLM orchestration, embeddings, re-ranking — with hands-on involvement in technical design and execution.

• GTM build: owned the full go-to-market stack — outbound, LinkedIn (organic + paid), content, and sales enablement.

• Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
• Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
• Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
• Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
• Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
• Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
• Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
• Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
• Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
• Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
• Integration of RESTHeart to create a REST API for MongoDB.
• Build an Angular frontend to simplify data queries and master data maintenance.
• Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
• Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

• 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).

Loom and Bloom

Python · TypeScript · n8n · Claude Code · Whisper · Gemini · Supabase · Notion · HubSpot · Digital Ocean

• Built an end-to-end content pipeline: one Loom video → marketing images, bilingual LinkedIn posts, newsletter and Help Center updates.
• n8n webhook → SSH → Claude Code session on a Digital Ocean VPS; three MCP servers (video, Notion, Supabase).
• Whisper word-level transcription, ffmpeg screenshots, Gemini UI annotation, PIL device mockups.
• Next.js upload UI plus a bilingual newsletter composer with HubSpot push.

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.

Project content: The use of “Generative Pretrained Transformer” technologies (GPT) will massively change the world of work in the coming years. ITZBund is developing a base service for this, which can be used by its 200 customers. This base service is offered as an on-prem and a cloud variant. Started in 12/23, from 05/24 productive systems (based on MVP) could already be rolled out and operated successfully for several agencies and federal ministries.

Project metrics:

• Budget in 2024 approx. €20M
• approx. 70 people in the core project context
• regular (indirect) contact with approx. 80 people in the wider project environment

My main tasks:

• Overall responsible external project manager
• Project definition phase/project setup and establishment of the project organization
• Strategic and operational project planning, as well as shaping and ongoing adjustments of the project
• Ongoing coordination with project owners and stakeholders in ITZBund
• Ongoing coordination with subproject managers, agile roles and other project staff
• Responsibility for project controlling, quality management, risk management and change management
• Responsibility for providing the base service on on-prem and cloud environments and its continuous development
• Planning and leading several hypercare phases
• Collaboration and ongoing coordination with a partner project of a federal ministry and provision of an MVP
• Coordination with agile teams
• Coordination with operations, release and deployment
• Responsibility for the implementation of the first customer projects
• Responsibility for creating an AI governance
• Ensuring accessibility (BITV)
• Responsibility for setting up a subproject for marketing activities
• Support in building an efficient proposal process
• Stakeholder management

Methodology:

• V-Model XT ITZBund
• Scrum

Tools used:

• Microsoft Office, Skype, MS Project, Confluence, Jira, SharePoint, Miro

• Integrated LangChain middleware between AEM and SAP PIM system
• Developed a FastAPI interface for system communication
• Implemented vector embeddings for semantic product search
• Evaluated LLM models (Vertex AI/Gemini, LM Studio, Hugging Face, OpenAI) for product analysis
• Developed an AEM component to display product recommendations and integrated the recommendation API into the AEM authoring process
• Designed and implemented Pinecone vector database for product embeddings
• Optimized response times and caching strategies
• Evaluated Vertex AI Studio for LLM testing and prompt workflows
• Implemented secure API routing and access control for AI components via FastAPI and gateway validation

• 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.

• Set up the go-to-market for Metyis DE by defining productized offerings that enabled onboarding of 5+ enterprise clients
• Successfully ran client projects, like ramping up the global marketplace business at a European consumer electronics company and scaling the DACH operations of two Dutch e-commerce and a German B2B nutrition business

• Built and scaled a data-science price engine across 8 EU markets, lifting fleet margin by ~2% on a €1.5B+ base
• Launched a generative-AI insights platform in 29 countries, cutting manual analysis by ~40% and driving weekly actions
• Led a hybrid team and aligned 100+ stakeholders to refine pricing logic and accelerate rollout across regions
• Set up KPI governance with WBR/MBR rhythms, reducing decision latency by ~30% across European pricing teams

• 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

• Positioning & messaging for thesis on SEO semantics & syntax
• Hardware + software campaigns for real estate, retail & municipalities
• Developed sales and website content
• Close alignment with product, design & sales
• Introduction of new marketing processes and automations

• Led the development of a circularity assessment tool aimed to help architects evaluate and enhance building circularity from an early design phase
• Conducted an in-depth analysis of the existing tool to map user flows and decode the underlying calculation logic
• Facilitated structured discussions with a cross-functional team and domain experts to refine the problem statement and define project priorities to deliver within a 6-week agile sprint
• Delivered a clickable prototype with a simplified user experience, integrating AI-guided assistance with smart tooltips, an ML-based material suggestion/matching logic and a custom GPT for value-added insights
• Tool is currently under consideration for continued development support by N3XTCODER and AI consulting support from Civic Coding Consulting