Prompt Engineering 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.

• Concept & IT implementation of inbound B2C campaigns, BPM process documentation
• Cross-departmental implementation of multiple whitepapers / guides
• Setup of the customer journey with MS Dyn365 / CRM: forms, landing pages, campaigns
• Project controlling, process documentation, testing and training / coaching, analytics reports
• Hands-on implementation & on-time launch of all campaigns for digital new customer acquisition

• Advise organizations on integrating AI into project and program management practices, delivering measurable productivity improvements
• Designed and implemented an AI agent to improve project communication, transparency, and reporting quality

• 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

AI-supported development and PM acceleration with agentic workflows; deep reinforcement learning; fully automated 24/7 setup.

• Established a new venture focusing on AI-powered UX and rapid prototyping

• Leading the development of an AI-based prototyping framework and prompt optimization for product design

• Managing stakeholder relationships and strategizing go-to-market approaches

• Planning and creating coaching programs (to be launched in 2025) on AI-driven product discovery

• Developed internal processes enabling prototype creation in minutes

• Published best practices and insights on LinkedIn, driving industry interest and awareness

Development of AI-powered marketing and content workflows, concept development for scalable content processes, prompt engineering, and development of an AI-based text generator for scalable on-brand communication as well as creation of marketing and conversion copy.

• Strategic consulting and implementation of AI-supported automation solutions for service processes (email communication, document handling, review processing, voice agents)
• Technical orchestration through n8n (workflow automation), Claude and ChatGPT (LLMs), as well as custom API integrations into existing enterprise tools
• Measurable client results: reduced response times from hours to minutes, 8h/week less invoice processing, 75% fewer routine calls
• Classification of regulatory frameworks (GDPR, compliance) as part of client consulting
• Technical product ownership (backlog, roadmap, prioritization)
• Formal contract vehicle for freelance mandates, including 1&1 Telecommunications
• Setup & scaling of enterprise AI platforms

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.

Introduction: Companies today face two major challenges: maintaining a pristine Workday tenant AND simultaneously developing an AI strategy that goes beyond mere experimentation. With CloudMcFly, I combine 10+ years of experience as a Workday Architect & PMP with cutting-edge AI strategy consulting. I don't offer theoretical concepts; I deliver implementable solutions. Core Services:

• 1. AI Readiness & Process Audit Not every process requires AI. I analyze your HR/Finance processes (Workday & Non-Workday) to identify "low-hanging fruits" for automation and AI integration. Outcome: A concrete action plan including ROI calculation.
• ️ 2. AI Strategy & Governance Roadmap From vision to execution. Together, we define guidelines (Ethics, Data Privacy, EU AI Act) and select the right tools (ChatGPT Enterprise, Copilot, Custom Agents). Outcome: A secure roadmap approved by Management and IT.
• ️ 3. Workday Architecture & Optimization As a certified Architect, I ensure your Workday tenant remains stable while we integrate new technologies (Launch, PEX, Delivery Assurance).
• 4. Enablement & Workshops An "AI Driving License" for your teams. Hands-on workshops on Prompt Engineering, Automation (n8n/Make), and operational efficiency.
• Interested in Pricing & Packages? Visit [link] The AI chatbot on my homepage knows all my consulting packages and can provide immediate details on pricing and availability. Give it a try!

• 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

• 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.

• 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.