RDF Experts in Germany

UEF 3.0 · Semantic Government Overlay (SGO) · Autonomous Systems (UAS / dual use)

• Designed: Semantic Government Overlay (SGO) – AI-guided administration without replacing existing specialist procedures. Read-only semantic layer over registers and specialist processes based on the Federal Information Management (FIM). Decision authority remains with the case worker (architecture principle).
• Developed: Reference architecture with source-backed, derived statements (Executable Ontologies OWL/RDF/SHACL). Technically guaranteed purpose limitation and no-write-path principle in specialist data – auditable, without a central data pool.
• Anchored: Regulation as a design principle: EU AI Act (high-risk obligations for public-sector AI, fundamental rights impact assessment under Art. 27), GDPR, NIS2, and administrative automation limits (§ 35a VwVfG, § 31a SGB X) as technical control points in the architecture.
• Created: Methodical tool for pilot organizations: data pipeline assessment (phase 0), compliance blueprint, and management summary as a decision-ready package for public administration.
• Specified: UEF 3.0 as a successor architecture to TOGAF – decision paper, canonical ontology, six-layer architecture, read/actuate boundary, federation registry, terminology concordance, and release delta as a closed specification status.
• Architected: AI-native mission OS for autonomous UAS and ground robotics as a tactical layer on top of a separately approved autopilot. Run-time assurance according to ASTM F3269-21 (Simplex pattern): the verified safety controller keeps authority, the AI function provides suggestions.
• Designed: Three-tier architecture – Tier 0 autopilot with 650 Hz flight control on RTOS, Tier 1 AI OS with semantic world model and multi-agent cluster, Tier 2 swarm and ground mesh. Zenoh as the primary fabric, MAVLink as the only authenticated command path (single writer). Result: graceful degradation – loss of the mission, not of the aircraft.
• Secured: Two-gate chain on the read/actuate boundary – governance gate (can-question: AI Act risk class per actuation, enforced human oversight under Art. 14, immutable log) before the RTA safety monitor (is-it-correct question: flight envelope, geofence, energy reserve) with revert to the baseline controller.
• Anchored: Dual-use architecture with common core and build-time fork instead of runtime switch. Three separate legal levels: civil variant – UAS under the EASA Basic Regulation (EU) 2018/1139 with the limited applicability under Art. 2(2) of the AI Act, ground robotics under the Machinery Regulation 2023/1230 with the full high-risk obligation chain, Cyber Resilience Act for both; unarmed carrier variant as defense material under AWG/AWV and Dual-Use Regulation 2021/821 (BAFA approval); armed variant under KrWaffKontrG. Each variant lives under exactly one dominant legal regime. Evidence base: AI BOM, SBOM, and complete data lineage.
• Analyzed: System analysis and realignment of grown engineering system landscapes. Approach concept for consolidation without migration – semantic layer over the existing sources instead of data transfer. Result: decision-ready implementation concept including an evaluation model for the target architecture.

• Consultant for system architecture, AI agents & integration, coach for data & process logic, Graph-RAG approaches, security and data protection.
• On-premise AI solutions with high compliance and performance requirements.
• Architecture decisions, operational setup, strategic prioritization & deployment.
• Technologies: LiveKit JS SDK, LiveKit Agents, Web Audio API, JS, AudioWorklet, Loki, vLLM, Zscaler, Docker, Neo4j, MySQL, Python.
• Models: GPT-OSS 20B, Whisper large v3 turbo, Qwen3-TTS.

• Analysis
• Consulting
• Software design
• Development
• Automation
• Testing
• Deployment
• Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
• Developed best practices for working with agentic systems and AI in practice
• Created code templates

• Led cloud and data engineering organization, defining architecture strategy for next-generation data platforms
• Designed and delivered an automotive fleet data management system including scalable ingestion pipelines, signal catalog management, and campaign processing workflows
• Built cloud-native microservices and streaming architectures supporting real-time vehicle data and AI-powered threat detection
• Established engineering standards for data quality, security, lineage, and governance in alignment with ISO/SAE 21434 and GDPR
• Managed engineering teams across data, backend, cloud, and AI functions, ensuring consistent delivery of high-quality, production-ready solutions

• Project environment: Python, Java, Azure AI Studio & OpenAI Studio, embedding models, LLM as a judge
• Project language: German
• Project role(s): Project manager
• Project management for improving the performance of a chatbot
• Research and evaluation of approaches to improve and measure response accuracy and improve the chatbot's understanding of context
• Coordination of architecture decisions with the technical team and architects
• Coordination and transfer of research results into development tasks

• Analyzed large-scale power plant data to develop and optimize key performance indicators (KPIs) for fleet-wide performance monitoring.
• Designed and developed interactive Power BI dashboards to provide real-time insights into key business metrics, improving decision-making processes across departments.
• Collaborated with site engineers and asset management to harmonize performance metrics across multiple countries.
• Supported digital transformation initiatives by implementing data-driven use cases using agile project management methods.
• Utilized OSIsoft PI systems for time-series data analysis and visualization to improve operational insights.

• Connecting the in-house heat pumps to EEBus (in accordance with GEG §14a)

• Support for Limit and Monitoring Power Consumption use cases

• Linux Yocto 2.5.4 for armv5e / Yocto 2.5.4–4.3.3 for x86 target

• g++ 7.3–13.2

• boost 1.85 (Asio/Beast)

• dbus-cxx 2.5.1

• Boost.SML 1.1.11

• CMake build management

• KEO-Json-API 1.3.0

• ktest 4.12.0 (Python Robot test framework)

• Intent-driven approach to configure all models across all series automotive in all distribution markets of this car manufacturer
• Developed a customer-facing, conversation-driven integration layer to achieve 100% hallucination-free technical configurations
• Utilized Microsoft Azure AI Services: AI Foundry, Agent Service, AI Search; Prompt Shield Services; Content Security; Terraform; API Gateway; AI Gateway; Container Services; Azure Agent SDK; Agent Skills; RAG; MCP Servers and tools

• Led data engineering and machine learning efforts for large-scale Knowledge Graph creation of WorldKG
• Developed pipelines for heterogeneous data integration (OpenStreetMap, Wikidata, DBpedia) using Python and SQL
• Designed neural architectures (Transformers, adversarial networks) for schema and entity alignment
• Created scalable ETL workflows to harmonize structured and unstructured geographic information
• Applied semantic modeling and SPARQL-based querying for data fusion and governance
• Mentored master’s students on AI, spatiotemporal data, and Knowledge Graph topics, fostering practical experimentation and innovation

• API-first product design (OpenAPI 3, HAL) and development (NodeJS)
• Setting up DevOps pipelines with GitHub Actions in an AWS architecture
• Technologies: OpenAPI 3, REST, HAL, NodeJS, JavaScript, TypeScript, AWS, AWS Lambda, Docker, GitHub Actions

• Migration of the platform to X systems
• Analysis of changes regarding TCP/IP communication
• Handling error tickets
• Installation and configuration of a Xypro Security 1 intrusion detection system
• Setting up an SNMP v3 connection to send alert traps to Netcool (Tivoli)
• Installation and configuration of the RemoteAnalyst software
• Import of master data, schedule data
• Setting up and monitoring MQ pipelines to external partners