18.11.2025 aktualisiert
nicht verfügbar
Senior/Lead Architect, Lead Developer, LLM/GenAI, Embedded, WebRTC, Workshops, Digitalization
Assling, Deutschland Dipl. Ing (FH) Elektrotechnik - Automatisierungstechnik
Über mich
Experienced Project Lead, CTO & Architect with hands-on expertise in IoT, edge, AI/LLMs, video streaming (WebRTC), and backend systems (Go, Quarkus, Node). Skilled in UX/UI, observability, big data, and multi-tenant architectures. Driving innovation, strategy & AI adoption. Leading tech teams.
Skills
System ArchitectProject leadUX/UIKubernetes / Docker / AWSCTOLLMEmbedded/EdgeGolang (Go)Quarkuswebrtc
As a Project Lead, Lead Architect, CTO and Innovation Architect, I specialize in designing and implementing (hands-on) complex systems across a variety of domains including embedded, edge computing, backend development, machine learning and mobile. I use mainly Golang, Java, Quarkus, Node.js (NestJS), Python for machine learning and Angular for web development, Android and Ionic, Electron and Tauri for desktop & mobile applications. I am also working with IoT/Embedded and edge devices, sensors and have a complete hardware lab at my location available.
I have a deep background in video streaming technologies such as WebRTC/gstreamer - in specific PION and LiveKit - and machine learning frameworks including OpenCV, PyTorch, and TensorFlow.
Since 2023 i am working with LLMs and tools like ChatGPT, Claude AI/CLI, various open source LLM models for local solutions, the LangChain/LangGraph tool family, Ollama, vLLM and LM Studio and various vector databases. I am using local LLMs for privacy-focused use cases. I am proficient implementing Agents, different RAG and related solutions. I work with to MCP- and A2A-Agent use cases. I do workshops for companies covering "The Art of using AI Agents in your dev team" for example.
In nearly all my recent projects i introduced "Observability" solutions based on OpenTelemetry (OTel), Prometheus, Grafana, Jaeger, fluent-bit. I often introduce topics like application KPIs, metrics and then help the teams to implement respective code.
My role has often involved acting as a senior technical liaison for both local and offshore development teams, notably establishing successful collaborations with teams in India - including on-site visits in India. My services include designing interfaces for embedded/edge systems, mobile solutions, and multimodal UIs, with a particular emphasis on integrating speech dialog systems and developing NLP and LLM-related solutions for edge devices.
I also consider workshops and technical consulting for companies and teams for topics like Kafka, NATS, TimescaleDB, LLMs, GenAI, complex backends, Microservices etc. These are usually short-term projects and are not listed in my history.
- Core SkillsLanguages & Frameworks: Golang, Java, Rust, Typescript, Python, Quarkus, Node.js (Nest.js), Angular, Android, Ionic, Electron, Tauri.
- Technologies: Docker, Kubernetes, WebRTC, gstreamer, OpenCV, PyTorch, TensorFlow, LLM tools, OpenXR, CloudXR and models.
- System Architecture & Design: Complex system analysis, UX/UI design, big data system architecture, stakeholder requirement elicitation, offshore team management.
- Industry Experience: Embedded and edge computing, machine learning, video streaming, mobile application development, big data and IoT.
- Bridging the gap between technical and non-technical stakeholders to drive project success.
- Continuously exploring emerging technologies to enhance system efficiency and user experience.
Sprachen
DeutschMutterspracheEnglischverhandlungssicher
Projekthistorie
As Lead Architect, defining and implementing with the team a scalable cloud-based AR/VR/XR (audio/video) streaming platform. Defining and implementing the complete system architecture for AR/VR/XR client devices, the complete backend infrastructure and the integration of typical XR platforms like Unity, Unreal etc. Defining compliance and test cases and related test clients.
Evaluation of open source and commercial components. Developing use cases and adding related documentation and Jira stories. As such my role combines Business Analyst, PO, Architect and for some services and PoC also backend developer.
Main tasks:
Topics:
Evaluation of open source and commercial components. Developing use cases and adding related documentation and Jira stories. As such my role combines Business Analyst, PO, Architect and for some services and PoC also backend developer.
Main tasks:
- Beating demanding photon latency demands
- Defining and documenting requirements for the complete E2E solution
- Leading a small team of developers
- Evaluating data flows for XR use cases, low latency
- GPU scheduling, splicing, optimization techniques, CUDA
- Integrating WebRTC data communications with Unity, Unreal etc
- Integrating WebRTC-based multiuser conferencing
- Defining, implementing universal API SDKs, generic GenAI-/LLM-Gateways
- Working on generic interfacing to agents using MCP, A2A
- Evaluation of Agent tools like MCP, Google Agent Dev Kit
- Exploring MLOps topics and tasks
Topics:
- Quarkus
- Angular
- WebRTC
- Unity
- Unreal
- OpenXR
- CloudXR
- Python
- LLM
- MCP Agents
- A2A Agents
- Microservices
- API Gateway
- Camara API
- CloudXR
- OpenXR
- Prediction models
- Kalman Filter
- Docker
- Kubernetes
- Scripts
- Observability
- Portal
- Kafka
- PostgreSQL
- NATS
- TURN/STUN
- LiveKit
- PION
- BitWHIP
- gstreamer
- ElectricMaple
- ffmpeg
- Prometheus
- Grafana
- Jaeger
- OTel
- MLOps
- GPU scheduling
- CUDA
Implementing the strategic mobile app for a healthcare solution, which is targetting professional personel, helping caregivers and family caregivers. The app shall enable customers to customize the (complex) behavior of a dedicated hardware system as easy as possible. The solution is based on current Angular 19 and Ionic 8 to be able to use the latest Angular features in specific. The UI is designed/optimized especially for the specific needs of the customer base.
The app connects with an custom embedded system using REST API. The related server has been implemented by me prior to this UI/UX project.
Angular 19, Ionic 8, Capacitor, NgRX, Signals, Barcode Scanner, Internalization, zeroconf, Typescript, Android, iOS
The app connects with an custom embedded system using REST API. The related server has been implemented by me prior to this UI/UX project.
Angular 19, Ionic 8, Capacitor, NgRX, Signals, Barcode Scanner, Internalization, zeroconf, Typescript, Android, iOS
Leading the development and successful deployment initially of a Proof of Concept (PoC), followed by the launch of the final production-ready solution. The product is consisting of the backend business logic, and is running on an custom embedded device. The second part is the mobile frontend based on Ionic (Android, iOS), targeting a market entry in Q1, 202.
Defined and implemented a mobile application for iOS and Android using Ionic and Angular, integrating the mobile client with the embedded Golang server. Implementing specific system services for IR, BLE and 433 MHz subsystems. Implementing the backend with IoT and edge computing capabilities for real-time data processing. Starting to analyze different user behavior to provide even better user experience.
Implemented a automatic credentials (serial number, wlan details, ansible related details etc.) product generation flow based on n8n and Pocketbase. Implemented the completely automatic Ubuntu image and configuration system based on Pocketbase (Golang) business logic and Ansible for system setup and configuration.
Implemented a diverse range of sensor and radar technologies to enhance device interactivity and data accuracy to improve healthcare monitoring and diagnostics use cases.
Defined three LLM-based use cases and implemented the initial FTI pipeline based on MLOps scenarios and current tooling. The first use case uses the customer and internal documentation and uses a local model, Ollama, RAG, a vector database and a dedicated UI to allow users to chat with the AI Agent. Various fine-tuning sessions were part of this task. The second use case will analyze log data to improve our products. The third use case is based on radar technology to analyze user/patient health state etc. with a visual transformer model.
Planning closely with the startup team to ensure a seamless handover of the current solution, including detailed documentation and user guides created with Figma and wireframing tools.
Post-launch, continuing to provide strategic support and enhancements, currently dedicating approximately weekend 10 hours per month to oversee the application's performance and user feedback integration. Working on the preparation and implementation of the needed measures to support mandatory and some optional processes related to the BSI KRITIS rules.
Setting up team/company related tools (mailcow, n8n, nextcloud).Leading the development and successful deployment initially of a Proof of Concept (PoC), followed by the launch of the final production-ready solution. The product is consisting of the backend business logic, and is running on an custom embedded device. The second part is the mobile frontend based on Ionic (Android, iOS), targeting a market entry in Q1, 202.
Defined and implemented a mobile application for iOS and Android using Ionic and Angular, integrating the mobile client with the embedded Golang server. Implementing specific system services for IR, BLE and 433 MHz subsystems. Implementing the backend with IoT and edge computing capabilities for real-time data processing. Starting to analyze different user behavior to provide even better user experience.
Implemented a automatic credentials (serial number, wlan details, ansible related details etc.) product generation flow based on n8n and Pocketbase. Implemented the completely automatic Ubuntu image and configuration system based on Pocketbase (Golang) business logic and Ansible for system setup and configuration.
Implemented a diverse range of sensor and radar technologies to enhance device interactivity and data accuracy to improve healthcare monitoring and diagnostics use cases.
Defined three LLM-based use cases and implemented the initial FTI pipeline based on MLOps scenarios and current tooling. The first use case uses the customer and internal documentation and uses a local model, Ollama, RAG, a vector database and a dedicated UI to allow users to chat with the AI Agent. Various fine-tuning sessions were part of this task. The second use case will analyze log data to improve our products. The third use case is based on radar technology to analyze user/patient health state etc. with a visual transformer model.
Planning closely with the startup team to ensure a seamless handover of the current solution, including detailed documentation and user guides created with Figma and wireframing tools.
Post-launch, continuing to provide strategic support and enhancements, currently dedicating approximately weekend 10 hours per month to oversee the application's performance and user feedback integration. Working on the preparation and implementation of the needed measures to support mandatory and some optional processes related to the BSI KRITIS rules.
Setting up team/company related tools (mailcow, n8n, nextcloud).
Tech stack: Golang, Angular 19, Ionic 8, Capacitor, Typescript, Android, iOS, Debian, Linux, Ansible, Quarkus, PostgreSQL, Docker, embedded, C, Python, OpenAI API, Langchain, Langraph, RAG, LoRa, Agents, LlamaIndex, Weaviate, Chroma, Ollama, LM Studio, mlx-lm, 433 MHz, Bluetooth Stack, Bluetooth, BLE, IR, nRF52840, ARM, Authelia, Authentik, Traefik, Nordic, n8n, Mailcow, Nextcloud, Pocketbase
Defined and implemented a mobile application for iOS and Android using Ionic and Angular, integrating the mobile client with the embedded Golang server. Implementing specific system services for IR, BLE and 433 MHz subsystems. Implementing the backend with IoT and edge computing capabilities for real-time data processing. Starting to analyze different user behavior to provide even better user experience.
Implemented a automatic credentials (serial number, wlan details, ansible related details etc.) product generation flow based on n8n and Pocketbase. Implemented the completely automatic Ubuntu image and configuration system based on Pocketbase (Golang) business logic and Ansible for system setup and configuration.
Implemented a diverse range of sensor and radar technologies to enhance device interactivity and data accuracy to improve healthcare monitoring and diagnostics use cases.
Defined three LLM-based use cases and implemented the initial FTI pipeline based on MLOps scenarios and current tooling. The first use case uses the customer and internal documentation and uses a local model, Ollama, RAG, a vector database and a dedicated UI to allow users to chat with the AI Agent. Various fine-tuning sessions were part of this task. The second use case will analyze log data to improve our products. The third use case is based on radar technology to analyze user/patient health state etc. with a visual transformer model.
Planning closely with the startup team to ensure a seamless handover of the current solution, including detailed documentation and user guides created with Figma and wireframing tools.
Post-launch, continuing to provide strategic support and enhancements, currently dedicating approximately weekend 10 hours per month to oversee the application's performance and user feedback integration. Working on the preparation and implementation of the needed measures to support mandatory and some optional processes related to the BSI KRITIS rules.
Setting up team/company related tools (mailcow, n8n, nextcloud).Leading the development and successful deployment initially of a Proof of Concept (PoC), followed by the launch of the final production-ready solution. The product is consisting of the backend business logic, and is running on an custom embedded device. The second part is the mobile frontend based on Ionic (Android, iOS), targeting a market entry in Q1, 202.
Defined and implemented a mobile application for iOS and Android using Ionic and Angular, integrating the mobile client with the embedded Golang server. Implementing specific system services for IR, BLE and 433 MHz subsystems. Implementing the backend with IoT and edge computing capabilities for real-time data processing. Starting to analyze different user behavior to provide even better user experience.
Implemented a automatic credentials (serial number, wlan details, ansible related details etc.) product generation flow based on n8n and Pocketbase. Implemented the completely automatic Ubuntu image and configuration system based on Pocketbase (Golang) business logic and Ansible for system setup and configuration.
Implemented a diverse range of sensor and radar technologies to enhance device interactivity and data accuracy to improve healthcare monitoring and diagnostics use cases.
Defined three LLM-based use cases and implemented the initial FTI pipeline based on MLOps scenarios and current tooling. The first use case uses the customer and internal documentation and uses a local model, Ollama, RAG, a vector database and a dedicated UI to allow users to chat with the AI Agent. Various fine-tuning sessions were part of this task. The second use case will analyze log data to improve our products. The third use case is based on radar technology to analyze user/patient health state etc. with a visual transformer model.
Planning closely with the startup team to ensure a seamless handover of the current solution, including detailed documentation and user guides created with Figma and wireframing tools.
Post-launch, continuing to provide strategic support and enhancements, currently dedicating approximately weekend 10 hours per month to oversee the application's performance and user feedback integration. Working on the preparation and implementation of the needed measures to support mandatory and some optional processes related to the BSI KRITIS rules.
Setting up team/company related tools (mailcow, n8n, nextcloud).
Tech stack: Golang, Angular 19, Ionic 8, Capacitor, Typescript, Android, iOS, Debian, Linux, Ansible, Quarkus, PostgreSQL, Docker, embedded, C, Python, OpenAI API, Langchain, Langraph, RAG, LoRa, Agents, LlamaIndex, Weaviate, Chroma, Ollama, LM Studio, mlx-lm, 433 MHz, Bluetooth Stack, Bluetooth, BLE, IR, nRF52840, ARM, Authelia, Authentik, Traefik, Nordic, n8n, Mailcow, Nextcloud, Pocketbase