Real-Time Operating System Experts in Germany

• Building strong awareness within the company around nearshoring
• Engagement Manager in the Nearshore Competence Center
• Responsible for nearshore consulting, partner screening and technical onboarding, designing and implementing cooperation scenarios, change management, stakeholder management, participation in steering committees, and collaboration with IT and business units
• Tools used: Microsoft Office 365, Microsoft Teams, Azure Devops, Microsoft Sharepoint, Conceptboard
• Key results: Establishment of a nearshoring strategy, successful identification and implementation of outsourcing partnerships, delivery of change management and stakeholder management at the highest level

• Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
• Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
• Automated extraction and training processes with CI/CD
• Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
• Developed and optimized embedded software for automotive control units
• Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
• Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
• Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
• Developed and trained machine learning models using PyTorch
• Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
• Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
• Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
• Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
• Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)

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.

• Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
• Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.

• Built a modular C++ 20 embedded Linux acquisition system on a Raspberry Pi, synchronizing IMU and dual-camera data streams to sub-millisecond accuracy.
• Integrated retro-reflective and contrast sensors to trigger acquisition and detect gaps between sorter rails.
• Implemented SPI & I2C sensor communication, GPIO interrupt handling with libgpiod, and CSV & JSON output.
• Designed a multi-threaded acquisition pipeline and an SPSC queue between acquisition and writer threads.
• Built a Python/HTML/CSS based web-server and validated the prototype in a DHL warehouse for defect detection.
• Documented software behavior, configuration, and results for maintainable handover and further development.

Seqnaut and Sycosm (WIP) – Embedded Audio DSP & Synthesis Platform

• Embedded audio DSP framework — synthesizers, effects, drum machines; modular audio graph
• Sequencing: PianoRoll, StepGrid, MasterClock; 8-voice polyphonic synth; CLI + OSC
• Transient Detector — dual-envelope adaptive threshold, guitar attack detection
• Hardware: Teensy 4.1, custom AFE design tapping BOSS GX-100; MIDI FX integration

Label: C++23, C, Python, CMake, FreeRTOS, Teensy 4.1 (ARM Cortex-M7), Teensy Audio (Stoffregen), DaisySP, I2S/SAI, OSC, MIDI

PHP and React redesign of a web-based ERP system.

• Development of the header and parts of the navigation
• Development of various modules in React, e.g. calendar, chat, and various tables

• Spearheaded the EU market entry strategy for L3/L4 autonomous logistics vehicles, driving the technological localization and deployment of the parent company’s smart robotics portfolio.
• Orchestrated technical alignment between top-tier autonomous driving suppliers across China and Europe, translating complex client requirements into precise engineering specifications compliant with EU standards.
• Cultivated strategic joint R&D initiatives with leading European universities, research institutes, and enterprises, accelerating the transition of cutting-edge robotic concepts into commercial products.
• Directed the end-to-end architecture of intelligent warehousing solutions, guiding cross-functional teams in optimizing hardware integration for autonomous vehicles & robots, and overall system performance.
• Led the R&D of high-fidelity simulation and AI algorithms using NVIDIA Isaac Sim & Lab, establishing robust "Sim-to-Real" pipelines to train and validate dynamic path planning optimization, intelligent obstacle avoidance, and complex navigation stacks prior to physical deployment.
• Maintained hands-on oversight of the core system architecture, focusing on bottom-level performance tuning, AI model inference acceleration with TensorRT/ONNX Runtime, and sensor integration.

• Built custom Yocto Linux image with Docker support (NXP i.MX 6ULL)
• Developed distributed Python application (web interface, gas/temp control)
• Designed multiple docker containers for production and development
• Wrote documentation per IEC 62304

• Development and quotation of technical solutions
• Design of high-performance solutions
• Management of aftersales operations, teams and budget
• Documentation revision

• Add/create test specifications
• Conduct regression tests
• Conduct release tests
• Analyze Jira tickets
• Identify software defects and fix them with C#

• closing the safety case
• creation of meaningful traceability matrices and targeted test expansion
• transferring requirements into other database systems
• structuring the requirements, assessing their completeness, and setting up new test cases based on use-case–based test strategies and the results of internal traceability matrices
• partial automation of tasks within a requirement assessment tool
• tools and languages: MS Teams, reqIF, Enterprise Architect, Codebeamer, proprietary tools for creating and evaluating traceability matrices (approx. 1000 x 1000)

• Analysis of legacy codebase and identification of architectural issues, implementing improvements in coordination with the Product Owner.
• Development of clean, efficient, and fully documented code following established software engineering practices and standards.
• Analysis of Erlang components in backend and device layers to provide recommendations for ensuring stable system operation.
• Setup and optimization of CI/CD pipelines on client infrastructure, including testing, debugging, and certificate management quality assurance.
• Participation in planning, design, and implementation of epics and stories according to Product Owner specifications.
• Technical consultation for Product Owner regarding Erlang codebase management and best practices.
• Collaboration with Product Owner, Scrum Master, and development team to ensure timely delivery of features.
• Elixir & Phoenix + PostgreSQL
• Erlang
• IoT
• CI/CD
• Git
• Agile/Scrum
• Docker
• Kubernetes
• Frontend (VueJS)
• Embedded Devices

• Led 14 member dev team and delivered postgresql database integration and performance optimization
• Delivered 8 K lines of C code with fewer defects (5 medium to low) in 8 months of development
• Integrate open source pgVector for AI application of the database for exact and nearest neighbor search