Career Profile
Quality engineer and AI architect based in Frankfurt am Main. Quality engineering and AI architecture run in parallel for me rather than in sequence: the testing discipline is the reason my agent system has gates and evidence instead of acting blind.
Publicly verifiable
The invoicing pipeline
Outbound and inbound invoices for an online job marketplace run in production between SAP Business ByDesign and the Peppol network. Inbound arrives over two parallel routes, a webhook and a mailbox, and both end in the same layer.
EN 16931 · Peppol BIS 3.0
client under NDA
The agent on this page
It answers questions about this CV. On a subject-matter question it does not guess: it consults a second agent over the open A2A standard on its own initiative, and marks that answer as coming from elsewhere.
mboiman.bks-lab.com/.well-known/agent-card.json
open-bridge, the open-source layer
The generic scaffolding behind this way of working is public under MIT: operating manual, skills, rules, agent runtime. Mandatory checks run before every merge, one of them a scan for leaked content.
github.com/bks-lab/open-bridge · MIT
The translation gateway
A stateless translator exposes the same agents to MCP clients. Three tools, no model of its own, no logic of its own, and no state carried between two calls.
MCP to A2A
open to MCP clients, anonymous gets lookups only; not a page you can click
In enterprise projects (DB Vertrieb, DVAG, TÜV Süd) I worked on quality monitoring, AI-driven test automation, and legacy migrations secured by continuous validation.
Today I combine quality engineering with AI architecture: MCP and A2A protocols, a network of agents that consult each other, and a production e-invoicing platform to EN 16931. The generic framework behind it is open source under MIT, and one of those agents answers this very CV, live.
I also pass this knowledge on, in developer and business workshops and in an invited talk at TU Darmstadt, translating hands-on AI for technical and non-technical audiences alike.
Projects & Solutions
Innovative AI solutions and automation tools
Enterprise Integration
E-Invoicing Automation Platform
Production E-Invoicing Pipeline (Peppol/EN 16931)
Core Features · Impact
Core Features:
- Peppol Network Integration: connection to the European e-invoicing network through a certified Access Point
- Multi-Format Support: ZUGFeRD, Factur-X, XRechnung, UBL 2.1, CII (EN 16931 compliant)
- ERP Integration: Automatic transfer to SAP ByDesign with validation and error handling
- Hexagonal Architecture: Domain-driven design with Ports & Adapters, seven repositories
- More test code than production code: around 46,700 lines of application code against 81,600 lines of tests, roughly 2,900 automated test cases. Every one of the seven repositories enforces a minimum test coverage in CI, 75 percent in the core service
- Format coverage: ZUGFeRD and Factur-X from 2.0.1, XRechnung in UBL and CII syntax, UBL 2.1, PDF/A-3 with embedded XML, plus a format detector that tells the variants apart by namespace
- Dual-Channel Inbound: access-point webhook (push) + email inbox (pull) for redundant processing
Impact: Production system: Peppol connectivity per EN 16931, automated inbound and outbound invoice processing through to the ERP. The outbound route runs 13 processing steps from the SAP document to Peppol delivery, each independently testable.
Quality Engineering
24/7 Automated Legacy Migration Validator: Dual-Run Quality Engineering
Enterprise Legacy Transformation with End-to-End Quality Assurance
The Challenge · The Solution · Result
The Challenge: Complex mainframe price calculation with millions of combination possibilities needed secure migration, without quality loss or data errors.
The Solution: 24/7 Dual-Run Validation System with intelligent deviation analysis
- Continuous Testing: Randomly generated tests across all variations around the clock
- Real-time Monitoring: Live dashboard with deviation detection on every deployment
- Complete Traceability: Every deviation uniquely logged with technical and business context
- Automated Root Cause Analysis: Direct access to calculation paths and error sources
- Quality Intelligence: Kibana dashboards for end-to-end quality overview
Result: Secure migration without data loss, 24/7 validation across all combinations and full transparency over migration status
AI & Machine Learning
Agent mesh and MCP-to-A2A gateway
Three agents, one translator, one open protocol. The agent on this page is one of them.
Problem · Solution · Result
- Problem: An agent that only holds its own knowledge guesses at everything outside it. Loading it up with someone else's knowledge does not make it smarter, only more expensive and harder to check.
- Solution: Three agents, each with its own subject, a person, a company and an open-source project, consult each other over the open A2A protocol, read-only. In front of them sits a stateless gateway that offers the same agents to MCP clients as three tools, with no model and no logic of its own.
- Result: A subject question gets passed on instead of guessed, and the borrowed answer is marked as borrowed. The agent card is publicly retrievable, and the generic framework behind it is open under MIT.
Quality Engineering
Quality Dashboard · Real-time Overview
End-to-end solution for real-time quality monitoring: the test tools of several teams and environments under one surface, plus multi-source integration (Pipeline, LeanIX, Jira, Confluence) for automated PDF reports with open issues, bugs, and technical debt.
AI & Quality Engineering
AI-driven Test Automation
Guideline-based AI agent in the IDE that generates tests directly from Jira stories.
Problem · Solution · Result
- Problem: Deriving test cases from a story is manual work, and it comes back with every story. A language model without constraints invents steps and selectors the application does not have.
- Solution: The agent reads the story over the Jira API and writes the test from it. The project's own conventions, page objects, naming rules and permitted selectors, sit beside it as a rule file and bound what it may produce.
- Result: The test comes out in the style of the existing Playwright project. Typing an empty file turns into reviewing a draft.
AI & DevOps
AI Context Orchestrator: Multi-Repo Development Platform
Central Control System for Enterprise AI Development
Core Features · Impact
Core Features:
- Context Orchestrator: a three-way split registry (instance base, shared org fragment, personal fragment) for repos, customers, infrastructure and skills, so company data and personal data can be distributed separately
- One skill tree for every tool: discovered alike by Claude Code, Codex, Copilot CLI and Cursor through committed symlinks. Log analysis, deployment verification, incident handling, email management, meeting transcription, PDF/certificate generation, SharePoint integration
- Hook-based Permission System: Smart Guard with project-specific security rules (branch protection, secret detection, deployment gates)
- Structured Work Tracking: Automatic documentation with daily/weekly archiving and cross-project Kanban board
- Cross-Repo Automation: Seamless navigation and operations across customer, partner and internal repositories
- Open-source layer: three-tier architecture. The generic CORE layer is public as bks-lab/open-bridge (MIT, with a live demo site) and is fed from this seed instance, with a BKS overlay in between.
Impact: In daily use: 102 skills, 5 sub-agents, 13 standing orders, 87 automatically filed meetings. Six mutually isolated instances share the same open-source core without data flowing between them.
Training & Workshops
Developer Workshop: AI-Agent Development & Integration
Target Audience: Developers, Technical Leads, DevOps Engineers
Workshop Content · Deliverables
Workshop Content:
- Hands-on AI Agent Development: Live coding with Claude, Gemini, GPT-4o
- Next-Gen Protocols: MCP & A2A implementation for enterprise integration
- Browser Automation: Playwright-MCP for automated test generation
- IDE Integration: AI assistance workflows with Claude Code as primary stack, GitHub Copilot & VS Code as tool comparison
- Multi-Agent Orchestration: JSON-RPC, secure agent communication
- Practical Demos: Jira integration, Elasticsearch queries, GitHub workflows
Deliverables: Complete source code, slides, hands-on exercises, production-ready templates
Consulting & Strategy
Enterprise AI Consulting: From Strategy to Implementation
Target Audience: C-Level, Management, Product Owners, Business Analysts
Consulting Services · Deliverables
Consulting Services:
- Strategy Development: From AI vision to measurable implementation roadmap
- Process Analysis: Identification of automation potential, quality-first assessment
- ROI Assessment: Business cases, cost savings, amortization calculations
- Executive Workshops: Hands-on strategy sessions with live-demo business cases
- Change Management: Team adoption, training concepts, resistance management
- Implementation Support: From concept to productive AI solution
Deliverables: AI strategy template, ROI calculator, implementation roadmap, executive workshop, implementation support
Healthcare AI
Medical Transcription System
GDPR-Compliant Transcription of Doctor-Patient Conversations
Core Features · Impact
Core Features:
- Fully local: No cloud uploads, all processing on local machine (Privacy-by-Design)
- Speaker Diarization: Automatic doctor/patient distinction with Pyannote 3.1
- SOAP Protocol Generation: Automatic creation of structured medical protocols
- Open Source AI: Ollama with Llama 3.2 for intelligent summaries without API costs
- Apple Silicon Optimized: Native performance on M1/M2/M3 Macs
Impact: Privacy by design, with fully local processing (no cloud upload), automatic speaker separation and structured SOAP protocols.
E-Learning & Compliance
AI Learning Platform
EU AI Act Compliant Learning Platform for AI Workshops
Core Features · Impact
Core Features:
- Article 4 Compliance: EU AI Act compliant certifications with audit trail
- Learning Card Types: Interactive quizzes, exercises, adaptive tests with scoring system
- Visual Themes: Learning, AI-Team, Star Trek, Nerd, Helldivers, Wednesday
- Progress Tracking: localStorage-based tracking with HR export functionality
- Automated Tests: learning paths and certification logic are covered by regression tests
Impact: Around 18,400 lines of application code and 16,100 lines of course material across 524 versioned files, written single-handedly (116 of 117 commits, the remaining one from a bot).
Quality Engineering
Django SLA Dashboard · Enterprise Service Monitoring
Enterprise SLA Monitoring Dashboard for Service Level Agreements
Core Features · Technical Highlights · Context
Development of a comprehensive SLA monitoring system as part of a BKS evaluation project (2023-2025) for monitoring Service Level Agreements with real-time metrics and automated alerting.
Core Features:
- Service Registry: monitored services, each with its own SLA target and check interval
- Response Time Capture: periodic measurement per service, stored as a time series
- SLA Evaluation: availability against the configured target, rolling over selectable windows
- Incident Management: capture and prioritisation of open incidents
- Performance Analytics: response time distribution as a histogram over configurable buckets
- Availability Timeline: history over a selectable window at a chosen resolution, with trend display
- Alert System: rule-based alerting on SLA violations and performance degradation
Technical Highlights:
- Django 4.2 with async views for high-performance data processing
- PostgreSQL with TimescaleDB extension for time-series data
- Django Background Tasks for asynchronous processing
- WebSocket integration for live dashboard updates
- RESTful API for service integration and CSV export
- Docker-based deployment with Kubernetes-ready architecture
Context: Internal evaluation and demonstration dashboard fed by simulated SLA data, an architecture reference for time-series monitoring (not a production system).
Quality Engineering
QualityCluster
QualityCluster is an alliance of quality engineers jointly developing better solutions in software quality assurance.
Our goal is to implement more quality through a higher degree of automation.
AI & Machine Learning
Streamlit demos: NLP text analysis
Interactive Streamlit demonstrations for NLP text analysis and AI-driven document processing.
Mobile & Web Development
wecation
wecation is a collaborative solution for organising shared experiences: plan, search, coordinate with friends or colleagues and enjoy the activity together! Built with Flutter and Google Cloud.
AI & Machine Learning
AI-Driven Chatbot for Trade-Fair Interaction
Development of an interactive chatbot using RAG (Retrieval-Augmented Generation), OpenAI GPT-4, Vector Embeddings, Python FastAPI, and Streamlit frontend to optimize user interaction on trade-fair websites.
AI & Machine Learning
Appointment enquiry over WhatsApp and phone
Two channels on one domain core: a WhatsApp assistant and a phone assistant that read free slots live from the booking portal instead of inventing them.
Problem · Solution · Result
- Problem: Calls were lost during working hours, and a language model must never invent an appointment.
- Solution: The model runs the conversation, the free slots come live from the portal API. It pencils an appointment in but never books it, and consent comes before the first substantive sentence.
- Result: The WhatsApp channel has been in production since August 2026 on the client's own business account, with data residency in Germany and real end users. The voice channel is built and verified and is deliberately idle at present.
AI & Machine Learning
Email Classification and Processing Workflow
Complete automation solution with Microsoft Graph API, SAP RFC, Azure Functions (Python), OpenAI GPT-3.5-Turbo, Elasticsearch 8.x, Kibana Dashboards, Docker containers, and GitHub Actions CI/CD.
Professional Experience
Senior Quality Engineer · Test Automation and Monitoring
TÜV Süd
Quality Engineering for an enterprise platform for AI-powered document processing in medical device certification (MDR/IVDR) with AI chat integration and data pipeline automation.
Key Responsibilities
- BDD API Test Framework: Development of comprehensive test framework with Python/Behave, self-healing authentication, HTML report generator with embedded API responses
- E2E Test Automation: Playwright E2E test suite for complete user journey (Setup -> Upload -> Transformation -> Download) with 500 error handling and route mocking
- Performance & Monitoring: Development of Azure Monitor Workbooks (QA Live Testing Companion, Error Investigation Assistant) with KQL for end-to-end monitoring
Key Responsibilities · Tools & Technologies
Key Responsibilities
- Bug Analysis & Quality Intelligence: Implementation of comprehensive bug tracking system with WIQL queries, automated dashboards, and executive PDF reporting
- Pipeline Integration: Test automation in Azure DevOps CI/CD pipelines with HTML report upload to Azure Test Results
- AI-Powered Automation: Development of automation skills for workflow optimization, bug analysis, and incident response with measurable time savings
Tools & Technologies
- Testing: Behave, Playwright, TypeScript, Python, pytest, Page Object Model, Fixtures
- Monitoring: Azure Monitor Workbooks, KQL, Application Insights, Log Analytics
- Backend: FastAPI, Python, Pydantic, SQLModel, Azure Functions, Azure Service Bus
- Frontend: React, TypeScript, Vite, TanStack Router
- AI Integration: Azure OpenAI (GPT-4o), LangChain, Azure AI Search, Embeddings
- DevOps: Azure DevOps Pipelines, Git, Docker, Azure Blob Storage, Poetry
LLM Infrastructure Architect & Automation Engineer
BKS · AI Research & Development
Development of LLM-orchestrated Enterprise Development Ecosystem: Fully integrated system for knowledge management, project automation, and development workflows with Claude, Gemini, and OpenAI GPT.
Key Responsibilities
- Open-Source Agent Infrastructure: open-bridge under the MIT licence, public at github.com/bks-lab/open-bridge, with a live demo site. The generic CORE layer (agent runtime, MCP gateway, skill tree, promote flow with a content-leak gate in CI) grows out of my own instance and is published upward by scope: 35 skills, 23 rules, 796 automated test cases across 51 test files, 30 releases since June 2026. Attack fixtures for path traversal, secrets and malformed overlays run as their own CI stage
- Data separation as architecture, not as agreement: separate instances per client, a rule forbidding access between instances, and a fail-closed contract for materialising shared configuration (the sync aborts rather than writing on doubt). Protection against data leakage at three points: pre-commit hook, pre-push hook and three validators in CI. Plus an adversarial simulation that attacks the protection itself on a schedule, as its own CI job in a sealed sandbox
- A2A Agent Network: an a2a-sdk server with a local claude backend, running on a runtime adopted from open-bridge. Three agents (person, company, OSS project) consult each other on read-only edges, fronted by a stateless MCP-to-A2A gateway so MCP-only clients can reach them. Agent Cards at protocol version 1.0, JSON-RPC 2.0. Around 4,600 lines of Python with 98 automated test cases, built as a team effort (four further contributors)
Key Responsibilities · Tools & Technologies
Key Responsibilities
- OpenTelemetry-based telemetry: logs, metrics and traces over OTLP into a managed sink, with a fixed field convention and naming scheme, a capped ingest key and read rights separated per client (measured as an access cut, not documented as an intention). Honest maturity: the log path is in operation, metrics and traces are wired and not yet accepted
- Human gate on four autonomy layers: what an agent may change about itself is split into four layers (context, skill files and rules, user profile, model weights), each with an explicit human gate. Proposals go through a dedicated folder with a schema and a review path instead of taking effect silently. The EU AI Act is therefore not just workshop content here but my own operating practice
- Specification and verification design: before building, a fixed sequence runs through requirement, design and verification plan. Every acceptance criterion is mapped to the strongest available check, in this order: deterministic test before external signal before model judgement, together with the gate strength. This is where quality engineering and AI development are the same discipline
- Git Wiki Transformation: Migration of Confluence knowledge base to structured Git-based wiki with hierarchical organization and multi-repository architecture (submodules for customer projects)
- MCP Navigation Server: Development of Model Context Protocol server (Python) with intelligent hierarchy navigation, automatic content discovery, and SharePoint integration
- Google ADK (earlier stage): the first version of the agent network ran on the Google Agent Development Kit with LiteLLM. Replaced in July 2026 by the a2a-sdk server on our own runtime; the ADK and tool-integration experience from that stage stays
- GitHub Project Automation: LLM-driven issue management with 7-mandatory-field system, automatic categorization, smart repository mapping, and review queue management
- Development Workflow Automation: GitHub Actions workflows for fully automatic issue-to-PR transformation (assignment → feature branch → implementation → code review → PR)
- Self-Documenting System: LLM automatically writes project status, meeting protocols, and issue updates back to Git wiki; closed loop of wiki reading → work execution → results documentation → Git commit with structured logging
- Work-time capture: commit-based work time analysis with automatic project assignment. The first stage ran against Clockify; since July 2026 it is being rebuilt as a private registry of semantic conventions on OpenTelemetry Weaver, with three layers (raw observation, derived block, billable time), canonical JSON serialisation per RFC 8785 and an integrity profile per OCSF
- Claude Code Plugin Ecosystem: 102 skills in the productive instance, discovered alike by Claude Code, Codex, Copilot CLI and Cursor through committed symlinks: log analysis, deployment verification, incident handling, email management, meeting transcription (87 filed meetings), PDF/certificate generation, SharePoint integration. Every skill carries one of four scope tiers (core, org, personal, user) checked by a CI validator; what may be published outward depends on it
- Context Orchestrator: central multi-repo control system with a three-way split registry (instance base, shared org fragment, personal fragment), so company data and personal data can be distributed separately. It carries repos, customers, infrastructure and skills, plus automatic cross-repo navigation and structured work tracking with archiving
- Hook-based Permission System: Smart Guard with project-specific security rules for Git operations: branch protection, secret detection and deployment gates
- Production Operations E-Invoicing: ongoing management of an e-invoicing platform for a leading online job platform (under NDA): automated incident management, log analysis, weekly reporting and go-live tracking
- Self-Operated Infrastructure: 50 launchd services on the central host, 17 devices in the registry, reachable over Tailscale rather than open ports. Backups follow 3-2-1 as a declared topology: 10 sources, 5 targets, 16 pipelines (restic/rclone to NAS), with actual state reconciled against intent.
Tools & Technologies
- LLM Orchestration: Claude (primary), Google Gemini, OpenAI GPT, Model Context Protocol (MCP), Google Agent Development Kit (ADK)
- AI Development Platform: Claude Code, Plugin Architecture (Skills, Hooks, Commands, Agents), YAML/Markdown Configuration
- Multi-Agent: A2A Protocol 1.0, a2a-sdk, Agent Cards, MCP-to-A2A gateway, JSON-RPC 2.0, SSE Streaming
- Backend: Python 3.13, FastAPI, uvx Distribution, Git Submodules
- Automation: GitHub Actions, GitHub GraphQL API, gh CLI
- Integration: SharePoint API, Microsoft Graph, Exchange Online, Elasticsearch, Kibana, OpenTelemetry/OTLP
- Betrieb: Cloudflare (Workers, Pages, R2, DNS), Tailscale, launchd, restic/rclone
- Development: Bash Scripting, jq, Docker, Multi-Agent Systems; orchestrated repos span Python, TypeScript, Rust and Go
AI-driven Automated QA Environment for Energy Infrastructure
AkkuSwap Startup
QA Leadership for EU-wide Battery Swap Infrastructure: AI-driven simulation tool for battery swap station network with energy infrastructure integration
Key Responsibilities
- QA concept, design and implementation for AI-driven simulation of EU-wide infrastructure
- QA framework development for inhouse AI server infrastructure
- Proof-of-concept for automated testing of AI-driven simulations
Key Achievements
- Established QA methodology for AI-driven infrastructure simulation
- Created testing framework for energy sector critical systems
- Validated proof-of-concept with measurable reliability improvements
Energy Sector Connection · Technical Stack
Energy Sector Connection
- 50 Hertz Participation: 50 Hertz (Elia Group subsidiary) participated in research project eHaul, the predecessor of AkkuSwap
- Direct energy sector experience with critical infrastructure quality requirements
- Context of critical energy infrastructure (transmission-grid environment)
Technical Stack
- Infrastructure: Linux, Docker, Azure OpenAI
- Automation: pytest, AI code generation integration patterns
- Monitoring: Grafana, Azure Monitoring
- Energy Systems: Battery swap infrastructure, grid integration simulation
Technical Lead & AI Automation Architect
BKS
Technical lead & development: Design and implementation of a scalable automation solution for processing e‑invoices and PDF invoices fetched from a mailbox of a leading online job platform (under NDA). Full responsibility from requirements analysis through implementation to production go‑live.
Focus areas
- Workflow automation: Extraction, analysis and classification of incoming invoices using Python and Azure Functions.
- E‑invoice classification & data extraction: Full processing in accordance with EU standard EN 16931 (ZUGFeRD, XRechnung, Factur-X), including structural and content parsing.
- Peppol Network Integration: Connection to European e-invoicing network through a certified Peppol Access Point for automated invoice receipt and dispatch with UBL/CII formats.
Focus areas · Tools & technologies
Focus areas
- SAP ByDesign Integration: Automatic transfer of validated invoice data to SAP ByDesign for seamless ERP integration.
- Hexagonal Architecture: Domain-driven design with Ports & Adapters for maximum testability and interchangeability of infrastructure components.
- Salesforce integration: Forwarding prepared invoice data via email to the downstream system for Salesforce import.
- Auditing & logging: Comprehensive documentation of all processing steps plus storage of original email content in the processing log.
- Outlook management & SharePoint archiving: Automatic moving and categorising of emails into predefined Outlook folders; archiving of original and generated invoice documents via Microsoft Graph API.
- Monitoring & logging: End‑to‑end real‑time monitoring with Kibana, including alerting and dashboard creation.
- Team coordination: Managing the developer team incl. code reviews.
- AI Workflow Automation: Development of AI skills for automating task tracking, issue management, and project documentation
- Intelligent Documentation System: Automated wiki synchronization with git commit analysis and natural language documentation generation
- Automated Status Reporting: AI-driven email updates with automatic project progress analysis
- MCP & A2A Protocol Implementation: Next-generation AI protocols for tool integration and multi-agent communication
Tools & technologies
- Azure Functions: Serverless orchestration of automation workflows.
- Python: Core logic for data extraction, classification and integration with automated CI/CD processes, packaging, and comprehensive unit and integration tests for quality assurance.
- E-Invoicing: Peppol Network, Access-Point-API, ZUGFeRD/Factur-X, XRechnung, UBL 2.1, CII
- ERP Integration: SAP ByDesign, Salesforce
- Microsoft Graph API: Access to Outlook mailboxes and SharePoint libraries.
- GitHub: Version control, CI/CD pipelines and collaborative development.
- Kibana: Monitoring, log analysis and performance dashboards.
- Streamlit: For developing an interactive web application for visualization.
- AI Automation: Claude AI, Custom Skills Development, Git Analysis, Natural Language Processing
- Protocols: Model Context Protocol (MCP), Agent-to-Agent (A2A), JSON-RPC 2.0
AI Engineering Lead & ML Solutions Architect
BKS on behalf of a client in the sustainability sector
Project management & technical lead: Development and deployment of an AI-driven email bot to automate customer communication, AI-assisted processing of incoming emails and triggering of follow-up activities in existing SAP systems. Responsible for end-to-end project coordination and team management from idea to implementation.
Focus areas
- Development of ML models: Design and training of specialized models for effective email classification, implemented via Azure Functions.
- API integrations: Seamless connection to Microsoft Outlook 365 via Microsoft Graph API and to SAP systems for customer data checks.
- Process automation: Automatic moving of emails based on classification and specific business rules, including complaint handling.
Focus areas · Tools & technologies
Focus areas
- DevOps and monitoring: Implementation of CI/CD pipelines and use of Kibana for monitoring.
- Development of a Streamlit-based user interface: Improved testability and visualization of process results.
- Team coaching: Technical guidance for developers with regular code reviews and problem-solving sessions.
Tools & technologies
- Azure Functions: For serverless application architectures and ML model execution.
- Python: Main programming language for the email bot and ML models.
- Microsoft Graph API: For accessing and processing emails in Microsoft Outlook 365.
- SAP API: For querying customer data and communicating with SAP systems.
- GitHub: For version control, CI/CD pipelines and collaborative development.
- Kibana: For real-time monitoring and system performance analysis.
- Streamlit: For developing an interactive web application for system monitoring.
Platform Quality Architect (CI/CD, Monitoring, Automation)
DVAG
Test management & coordination, as well as introduction of CI/CD with quality-gate setup. Acting as cross-team Quality Lead: onboarding, guidance, consulting and training for the introduction of quality metrics and test automation within the pipeline across teams.
Result: Real-time sprint-quality transparency via Grafana dashboards, quality-gated releases in the teams' CI/CD pipelines: data-driven release decisions replacing manual data gathering.
Key responsibilities
- Design and implementation of functional and technical dashboards with Grafana for real-time quality measurement
- Documentation of quality metrics covering the measurable sprint progress
- Design and setup of CI/CD pipelines for multiple development teams (backend + frontend)
Key responsibilities · Tools
Key responsibilities
- Introduction of quality gates to safeguard quality within the CI/CD pipeline
- Adoption of Gauge (BDD)
- Roll-out of Playwright and integration into the existing CI/CD pipeline
- Functional and technical incident analysis
- Consultancy, design and implementation of solutions for quality-related issues
Tools
- IntelliJ / Visual Studio Code
- Java / JavaScript / TypeScript
- Playwright / Gauge / Karate
- Python for processing and analysis of test and reporting data
- CI/CD / GitHub (reusable workflows)
- Helm / Containers
- JIRA / Confluence
- Grafana
- Behaviour Driven Development
- Consumer-Driven Contracts (Pact.io)
DevOps Quality Lead & Dashboard Architect
DB Vertrieb
Test management, test coordination, test automation, agile (Kanban, Scrum, SAFe): Coordination as PO of a cross-team QA team including cross-team onboarding, support, consulting, training
Key responsibilities
- Agile quality assurance through the design of quality gates in the CI/CD pipeline
- Design and implementation of functional and technical dashboards with Kibana, Graylog, Grafana, Instana & Elastic Search
- Introduction of domain-specific logging for microservices to transparently track calculations and service interactions
Key responsibilities · Tools
Key responsibilities
- Performance tests within CI/CD with Gatling / JMeter and analysis using Kibana, Graylog, Grafana and Instana
- Documentation of quality metrics on the progress of measurable sprint quality
- Design and setup of CI/CD pipelines for data tests
- Functional and technical problem analysis
- Consulting, design and implementation of solutions for quality-related issues
- Setup of Junit, unit tests and integration tests, among others with Java Cucumber
- Development of a hybrid app for presenting (reviewing) results on a web or mobile platform
- Coordination of integration tests between the involved teams
Tools
- IntelliJ
- Java
- Cucumber / Cypress
- JMeter / Gatling
- CI/CD / GitLab CI / Jenkins
- Helm / Docker
- JIRA / Confluence
- Kibana / Grafana / Instana / Graylog
- Behaviour Driven Development
- Consumer Driven Contracts (Spring Cloud Contract)
- Ionic (hybrid app)
- Selenium
Performance Engineering Lead & Test Architect
DB Systel
Load and performance tests and analysis (test manager, test designer, analyst): Development, execution and analysis of load and performance tests
Key responsibilities
- Requirements management
- Status reports
- Customer consulting
Key responsibilities · Tools
Key responsibilities
- Documentation in the form of test concepts, analyses, management reporting and wiki documentation
- Setup and execution of load and performance tests
- Analysis of results & performance logs
Tools
- Visual Studio
- Eclipse
- JMeter
- Git
- JIRA / Confluence
- Excel & Access VBS
- Interfaces: HTTP, WS, REST, MQ, JMS
Senior iOS Developer & Mobile Architect
Telekom
Senior iOS Developer: Further development of the rebuilt version of Deutsche Telekom's Customer Center App, extension with new functionalities for IFA.
Key responsibilities
- Swift
- iOS 8
- Requests & responses to backend
Key responsibilities · Tools
Key responsibilities
- Error handling
- iPad / iPhone
- Scrum
Tools
- Xcode
- Git
- JIRA
Quality Assurance Lead & Test Automation Architect
Siemens
QA, test management, test automation, requirements engineering: A total of 5 projects were supported as QA & test manager and successfully implemented
Projects
- SIPCA: Management system for employee annual targets and bonus calculation
- STM: Application and travel expense recording (Frontend: Web / Backend: SAP)
- GPM2: Share bonus program for employees (Frontend: Web / Backend: JBoss)
Projects · Key responsibilities · Tools
Projects
- PATAC
- SOM: Management and overview of OrgUnits
Key responsibilities
- Creation of technical test concepts and test plans
- Defect tracking
- Management of CRs, communication between customers and developers
- Creation, maintenance and scheduling of test automation
- User acceptance tests
- Training of new testers and test automation engineers
- Support as test manager after project completion in regular operation
- Setup of acceptance tests
- Transfer of functional specification and detailed specification to Quality Center
- Monitoring of implemented requirements against the detailed concept
Tools
- HP ALM
- HP UFT
- HP QC
- Quick Test Pro
Performance Test Engineer & Load Testing Specialist
ING-DIBA
Load and performance tester: The goal of the project is the design and development of LoadRunner scripts for load testing a bundle of approx. 20 J2EE applications in a banking portal
Key responsibilities
- Creation of test scripts in LoadRunner with the http protocol
- Execution of load tests and analysis of results
- Monitoring and reporting
Tools
Tools
- LoadRunner
Performance Test Engineer & Quality Consultant
British Telecom, Mobiliar, DB-Systel, Sparkassen Informatik, Loyalty Partner, Telekom, Itelium, Deutsche Post, Postbank
Load and performance tester / tester: Various projects, predominantly load & performance tests
Key responsibilities
- Creation of test scripts in LoadRunner with the http protocol
- Execution of load tests and analysis of results
- Monitoring and reporting
Tools
Tools
- LoadRunner
Speaking & Workshops
Guest Lecture: AI-Assisted Software Development in Practice
TU Darmstadt · Information Systems Group · Lab course “AI Startup: From Idea to Execution”
04/2026Invited guest speaker in a block lab course for Bachelor's students in Computer Science (Dept. 20). Shared how agentic coding and AI prototyping work in real projects. The talk connects directly to the students' subsequent team work on their own startup ideas.
Topics
- From prompt to prototype: AI toolchains that get an MVP done in hours, not weeks
- Agentic coding in practice: where it scales, where the friction sits, where tools break down
- Live demos from my own skill ecosystem, on demand in response to the group's questions
- Lessons from production AI projects rather than slide theory
Format: moderated impulse talk with interactive live demonstrations. Audience: ~25 students with mixed prior knowledge.
Workshop: Development with Generative Language Models
Developer Workshop · Agent-based Software Development
06/2025Developer Workshop: Comprehensive workshop on modern AI agent development with practical demos and live coding
Workshop Content
- Development with LLMs (Claude, Gemini, GPT-4o) and token optimization, used across projects
- Model Context Protocol (MCP) & Agent-to-Agent (A2A) protocol implementation
- Google Agent Development Kit (ADK) for multi-agent systems and tool integration
Presentation: Efficient Documentation through Automation
Enterprise Presentation · AI-powered Documentation Workflows
04/2025Enterprise Presentation: Demonstration of modern automation approaches for centralized, current documentation and sustainable project success
Key Focus Areas
- Single Source of Truth: Central truth in Confluence, avoiding redundant versions
- Shift-Left Documentation: Parallel code and documentation development for higher currency
- Automation First: Bots and CI/CD jobs for recurring tasks, automatic updates
Applications of AI in Business Context
AI Workshop for Business Integration
2023AI workshop: Comprehensive presentation and live demonstration of innovative AI applications for business with a focus on practical implementation, theory and code examples.
Presentation content
- Comprehensive overview of AI fields and patterns (supervised/unsupervised/semi-supervised learning)
- Deep dives into machine learning, deep learning and natural language processing
- Detailed look at GPT models and their functionality (next token prediction)