// AI Engineer
AI engineer for agent workflows, RAG pipelines and business automation.
My strongest positioning is AI engineering: agent workflows, document-analysis pipelines, local/private LLM systems, edge AI and practical automation that a business can actually use.
Available for AI engineer roles and freelance AI automation projects across Europe and the Middle East, with Stuttgart as my base.
Proof points
- Leading the EFQM/RADAR AI Report Analyzer, an agentic RAG pipeline for analyzing self-assessment reports and assessor outputs.
- Designs local-LLM workflows when confidentiality matters, avoiding external APIs for sensitive organizational documents.
- Built an edge AI computer-vision prototype using YOLO and GStreamer on embedded hardware during Porsche Digital work.
- Built an internal LLM routing system that uses local inference to decide when to use local models versus cloud providers.
- Uses AI-assisted engineering workflows regularly to design, build and validate custom software systems.
Relevant work
- May 2026 to present
EFQM/RADAR AI Report Analyzer
A local-first, multi-agent RAG pipeline for the first-pass desk assessment of EFQM-based organizational self-assessment reports. It retrieves assessment guidance, links findings to source evidence, flags gaps and conflicts, and produces structured draft feedback for human review. I lead the five-person team and enforce fail-closed scoring, typed agent contracts, reproducible run provenance, and CI evaluation gates.
Read case study - 2025 — 2026
LLM Routing System
An internal on-device LLM routing layer that classifies each prompt by task type and complexity before selecting a local model or cloud provider. My operational estimate was that roughly 65 percent of requests stayed local, reducing cloud AI spend by about 45 percent. These are self-reported estimates rather than figures from a controlled finance study.
Read case study - 2025 to 2026
Edge AI Computer Vision Prototype
Real-time object detection prototype using GStreamer and YOLO on embedded ARM and x86 hardware. The modular pipeline separates preprocessing, inference, post-processing, and visualization, then produces visual overlays and structured detection metadata for downstream feedback experiments.
Read case study - 2025
Autonomous Robotics System with YOLO + LiDAR
AI-driven robotics prototype combining computer vision and sensor fusion. YOLO for real-time visual recognition, LiDAR for obstacle avoidance and mapping, custom path-planning logic using detected objects as waypoints. Demonstrated in simulation and real-time experiments.
Read case study - Oct 2024 — Mar 2025
EUNO: ML-Based Mood Tracker (Graduation Project)
ML-based mood tracker with a Random Forest model that reached about 87 percent accuracy after feature engineering and SMOTE class balancing. A FastAPI backend runs the preprocessing and prediction path, while Flutter, SQLite, and Firebase support the application. Built with Jaser Quteshat under Dr. Ahmad Barghash.
Read case study
Search fit
- AI engineer Europe
- AI engineer Germany
- AI engineer Middle East
- applied AI engineer
- edge AI engineer