Visual Studio Code Experts in Germany

Creation of concepts for M365 implementation, especially Tenants, EntraID, EntraConnect and ExchangeOnline, taking BAS standards into account (mandatory baseline security requirements) in the project "Concept M365" with the aim of transferring the concepts to the M365 environments of Bitmarck and then handing them over to the customer.

• Creation of a current-state analysis of the existing M365 environments as well as the on-premises environments and the BAS standards.
• Creation of concepts for the topics Tenants, EntraID, EntraConnect and ExchangeOnline taking the BAS standards into account
• Design and implementation of an automated solution for creating standardized M365 tenants based on Microsoft M365 DSC (Desired State Configuration)
• Transfer of the concepts into the M365 environments
• Creation of detailed technical documentation

• Proactive support of management in business field development & innovation management

• New development of a suite of business applications for analyzing valuation, P&L, and market price risk data

• Automation of all internal and external business and work processes

• Development of AI-based and AI-supported ETL processes as well as data analysis

• Business use-case development

• Business and work process optimization

• Enterprise architecture management

• Sales data analysis and forecasting as well as capture

• Inventory management & reordering

• Supplier management and communication

• Customs processing & clearance

• Shipping handling & warehouse coordination

• Interface management

• Technologies used: Microsoft Office 365, Microsoft Teams, JTL-Wawi, JTL-WMS, OTTO Partner Connect (OPC), Amazon Seller Central, DHL Global Forwarding, Jira, Draw.IO, Java (8,17,21,25), Jenkins, SonarQube, Git, Gitea, Spring Boot, Spring Batch, Vaadin, H2, PostgreSQL, Docker, Local LLMs, Postman, JasperSoft Studio, JasperReports

Developed a CPU monitoring application using Spring Boot and React, containerized with Docker Compose, with automated infrastructure provisioning using Terraform on AWS.

Stack: Spring Boot, React, PostgreSQL, REST API, Docker Compose, Terraform, AWS

Project goal: Carrying out system tests and validating industrial communication and control systems, including requirements definition and verification.

Responsibilities:

• Test planning, test execution
• Test strategy, test cases, and test specifications
• Requirements analysis
• Ensuring traceability between requirements, test cases, and defects
• Carrying out regression and integration tests
• Defect analysis
• Simulation and validation
• NetSniffer Wireshark
• Supporting test automation (Python, CI/CD)
• Reporting
• Stakeholder coordination and agile collaboration (Scrum / SAFe / Kanban)
• V-Model
• CI/CD automation with Python, Groovy, and frameworks (Selenium, PyTest)

Technologies:: CAN, Modbus, Ethernet, PLC, PROFINET, Codebeamer ALM, Scrum, SAFe, MS Teams, Git, CI/CD with GitLab CI and TeamCity, Windows Batch, FAS, Wireshark, Python, Enterprise Architect (EA), AI tools (e.g. ChatGPT, Microsoft Copilot), VS Code, Tia Portal, SCL, Python (Selenium, PyTest)

• 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.

• 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

• Introduced a unified test strategy to establish quality gates and define test activities for the 'ICaruS' subproject of the Bundeswehr

• Built and implemented manual and automated tests, set up the test infrastructure, and introduced test management tools

• Established KPIs and dashboards to present and distribute test metrics to project team members and stakeholders

• Technologies used: Playwright, TypeScript, REST, PEGA Systems, Jira, HP ALM, GitLab Runner, Kubernetes, Git, VS Code, Angular, Scrum

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

• DTO class generator: In-house development of a tool for the automatic generation of classes, functions, and methods for use in .NET Core projects based on SAP Business One Service Layer metadata
• E-invoice import add-on: Development of a SAP Business One add-on / coresuite module for the automated import of ZUGFeRD e-invoices as incoming invoices – with configurable mapping to flexibly control the import logic across four mapping types (DIRECT, SQL, FIXED, EXPR). The add-on is available as a licensed product for SAP Business One customers.

Stack: SAP Business One, SAP Business One Service Layer, coresuite Country Package, C#

Independent product design practice supporting startups and growing companies with enterprise SaaS, workflow architecture, product strategy, and AI-enabled product development.

• Deliver end-to-end product design for digital products, websites, and SaaS platforms, combining product thinking, UX, visual design, and implementation.
• Apply AI throughout the product lifecycle—from research, synthesis, information architecture, and prototyping to development, SEO, and content strategy.
• Design and build production-ready digital products using AI-assisted workflows, enabling significantly faster iteration and delivery.
• Advise founders and businesses on product positioning, UX strategy, and digital transformation.

• Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
• Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
• Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
• Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
• Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.

• 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

• Automotive SPICE®: all assessments fully achieved
• Agile transformation: V-model → SAFe successfully implemented
• Series release: on-time, quality-assured software delivery for key Volkswagen Group models (including ECE homologation)
• Stakeholder management: internal & external
• Process optimization: implemented a continuous improvement process (CIP) with a tracking system

• 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).