Machine Learning Experts in Germany

Stabilizing an Azure/.NET landscape in live operation.

• Architecture, DevOps, and operational readiness; technical decisions under time pressure
• Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics

Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps

• Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
• Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
• Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
• Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.

• 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

Planning, execution & moderation of crisis communication and reputation management

AQIM is a non-profit organization based in Austria. An association of interim managers from Germany and Austria. Founded in December 2024. Multi-part training with workshops and consulting for preventive crisis communication, in cooperation with the founding members and those responsible for communication. To assess crisis potential for the market entry strategy and ahead of a digital campaign.

The project in a nutshell:

Consulting and training for the purpose of:

• Analysis of crisis potential as a new market entrant and its internet-based communication activities
• Planning and developing crisis communication with targeted measures
• Developing a crisis plan to minimize reputation risks in an emergency
• Conducting training and information sessions

Success:

• What started as a one-time consulting event for preparing an information campaign became a multi-part workshop on preventive crisis communication. This not only gave the organization expertise and confidence in the event of acute challenges, but also gave the participating interim managers new skills for their day-to-day work on assignments
• The workshop enabled those responsible to launch the information campaign that was being prepared at the time and planned for six months, as scheduled at the end of January 2026 on social media
• Careful scheduling and defining how to handle critical voices in terms of terminology and tone, as well as compliance rules, ensured a smooth start and run of a 3-month information campaign

• Task Engineering: Evaluating Coding Agents.
• Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
• Prompting & Translation: finding failure points where AI does not work, in German.
• Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
• Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
• Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
• Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.

• Further development of the existing e-commerce platform based on SAP Commerce (Hybris) to meet the growing demands of digital commerce.
• Ensuring the scalability and performance of the backend, so the platform remained stable and efficient even under heavy user load.
• Development and integration of new OCC REST APIs and services for modular extensions and flexible adjustments, to implement new features quickly.
• Optimization of data flows and interfaces, which significantly improved platform efficiency and system performance.
• Ensuring a maintainable and scalable code base by using Clean Code principles, proven design patterns, and a future-proof architecture.
• Reduction of errors through extensive testing with JUnit, Mockito, and load tests with Gatling, supported by the introduction of automated test processes.
• Improved system performance through targeted refactoring measures and efficient database queries, especially to handle peak loads.
• Use of modern cloud and monitoring tools such as Kubernetes, Google Cloud Platform (GCP), and Grafana to ensure a stable and monitored infrastructure.
• Clear improvement in efficiency, scalability, and reliability of the platform, which now meets the demands of a dynamic and growing e-commerce market.

• LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)

• Agentic AI pipeline for automated Scope 3 emissions calculation with 150,000+ validated data records

• Smart API workflows for real-time carbon footprint calculations in ERP and ESG systems

• ML algorithms to predict emission hotspots and optimize product design

• Automated data validation pipelines with NLP for quality assurance of CO₂e datasets

• Led a 15-person cross-functional team to develop 10+ AI features

• Strategic product planning and AI roadmap with 35% shorter time to market

• Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)

• On-time project delivery with 95% budget adherence through data-driven backlog management

• Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% team velocity increase)

• Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)

I founded NovoSign together with two partners. There I am responsible for strategic development and operational management, from the product all the way to the business model.

This experience is especially valuable for my work as an external CDO: I know the product and founder perspective from my own responsibility, not just from consulting projects.

• Consulting across two integrated areas: Commercial Strategy (pricing, sales steering, marketing strategy, market expansion, margin management) and Operational Efficiency (process automation, AI integration, workflow design, last-mile automation).
• Development of custom SaaS solutions, explicitly tailored to the specific requirements and processes of each company.
• Delivery of AI training and change management workshops for managing directors and specialist departments, including AI competence training with a certificate of attendance under Art. 4 of the EU AI Act.

• 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.

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

• A digital system made up of special software and hardware components. The overall system replaces the traditional process with job slips and handwritten notes and enables more efficient order intake. Orders and work steps for the violin-making company’s projects can now be recorded, processed and logged in real time directly on the workshop’s touchscreen PC, by mobile phone or on the desktop. This gives customers a more transparent view of the work on their instruments and allows them to track the status and progress of their instrument through their customer account.

Tech stack: next.js, React, Flutter, Dart, Raspberry, Linux, Directus

• 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

• Short description: Leading a group-wide project to establish standardized cloud governance for Microsoft Azure, including policies, security and compliance controls, automation, and cost and operations management while preserving the autonomy of decentralized business units within regulatory frameworks.

• Tasks and activities:

• Overall responsibility for designing, building, and implementing a company-wide cloud governance structure (Azure), including target picture, roadmap, and operating model.

• Managing internal and external stakeholders (C-level, IT, Security, Compliance, Cloud Architecture, DevOps), including decision and escalation management.

• Planning and facilitating workshops on cloud strategy, governance principles, and the design of areas such as identity, connectivity, and platform management.

• Defining, implementing, and rolling out cloud policies (Azure Policy / custom policies), security standards, and compliance requirements (including GDPR, ISO 27001, BSI C5).

• Building a cloud governance framework based on the Azure Cloud Adoption Framework (CAF), including landing zone and guardrail concepts.

• Introducing automation solutions for governance, security, and cost control (policy/control automation, IaC, CI/CD-based control mechanisms).

• Implementing cloud security and compliance monitoring mechanisms as well as continuous improvement processes.

• Establishing and operationalizing FinOps in an enterprise environment (central and decentralized FinOps teams), including cost management strategies, reporting, and guardrails.

• Integrating governance policies into DevOps processes (e.g. CI/CD principles for security and compliance checks, GitLab Runner concept in spokes, GitLab CI/CD for CAF landing zones).

• Implementing access concepts including RBAC design and breaking-glass mechanisms (emergency access) as well as certificate automation (ACME / step-ca).

• Achievements:

• Created a unified, auditable governance and control set for Azure (policies, standards, compliance mapping) and thus laid the foundation for scalable cloud use in a regulated environment.

• Established repeatable automation for governance, security, and cost control (IaC + CI/CD), reducing manual effort and implementation risk.

• Improved operational and decision-making capability across central and decentralized units (clearer roles, responsibilities, escalation paths, balance between autonomy and group requirements).

• Significantly increased workload compliance during lift-and-shift migrations.

• Technologies used:

• Microsoft Azure Policy, custom policies.

• Terraform, OpenTofu, Terragrunt.

• step-ca (ACME).

• Entra ID.

• Azure Firewall.

• Azure Networking, hub-and-spoke architecture.

• Azure vWAN (evaluation).

• Azure Front Door, Azure Application Gateway.

• Azure ExpressRoute.

• Azure Key Vault.

• NetBox.

• GitLab (on-premises).

• Infrastructure, concepts used:

• Cloud shared responsibility model.

• Hub-and-spoke connectivity / central shared services (from hub-spoke context).

• Central governance with decentralized delivery (business unit autonomy with guardrails).

• Methods used:

• Scrum.

• Stakeholder management (C-level to engineering).

• Cloud governance, Azure Cloud Adoption Framework (CAF).

• DevOps, CI/CD.

• Cost and FinOps approaches: tagging/chargeback models, budget/alert concepts, reserved instances/savings plans vs. on-demand scenarios, sensitivity analyses.

• RBAC, breaking-glass concepts.

• ACME / certificate automation.

• GitLab Runner concept in spokes, GitLab CI/CD pipelines for CAF landing zones.