Applied AI & Intelligent Systems
RAG systems, LLM applications, agentic workflows, and intelligent automation.
- Retrieval
- Reasoning
- Automation
Applied AI Engineer Germany
Systems. Models. Real-world problems.I design and engineer intelligent systems across machine learning, computer vision, and applied AI—turning complex problems into reliable, practical solutions.
01 / Expertise
RAG systems, LLM applications, agentic workflows, and intelligent automation.
Object detection, semantic segmentation, anomaly detection, and visual quality inspection.
Predictive modelling, experimentation, statistical evaluation, and scalable ML pipelines.
02 / Selected Work
Intelligent systems,
engineered with purpose.
Query-aware routing
A production-oriented retrieval-augmented generation system for answering questions across industrial documentation with measurable retrieval quality.
Explore case study : Industrial Knowledge CopilotDeveloped and evaluated a reference-based industrial inspection pipeline using DINOv2 to identify visual anomalies in manufactured components. The workflow combines pretrained visual representations, ROI-aware processing, anomaly scoring, and calibration-driven evaluation.
Explore case study : Industrial Visual Anomaly DetectionJune 1, 2024 – May 31, 2025
An evaluation-driven forecasting workflow that transforms reservation transactions into daily room-night demand, compares forecasting strategies through rolling-origin backtesting, and generates channel-level predictions with uncertainty-aware planning scenarios.
Explore case study : Forecasting & Decision IntelligenceAn AI agent that investigates nearly 5 million infrastructure log records and produces evidence-grounded answers tailored to engineers, team leads, and managers.
Python / Agentic AI / SereneDBAn industrial computer vision system for identifying and segmenting manufacturing defects using YOLOv8. The project combines polygon annotation refinement, targeted augmentation, GPU-accelerated training, and class-specific evaluation to investigate segmentation performance and precision-recall trade-offs.
Python / PyTorch / Ultralytics YOLOv8A modular semantic segmentation framework combining DeepLabV3, configurable preprocessing, systematic model evaluation, and deployment-oriented inference design.
PyTorch / DeepLabV3 / ResNet-5003 / How I Approach Engineering
Start with the right question.
Make the problem explicit before choosing a model. Clarify what a useful outcome looks like and how to evaluate it.
04 / Professional Background
A foundation in engineering,
across AI, software, and data.
Data & Model Engineer
Building applied AI systems across LLM workflows, computer vision, model APIs, and large-scale data processing.
Research Assistant
Academic research at the intersection of data science, machine learning, and software engineering.
Data Scientist
Worked on analytics, experimentation, funnel optimization, dashboards, and data-driven product improvement.
Associate Software Engineer
Built and improved software and data workflows with an emphasis on reliability, performance, and delivery.
Data Analyst Intern
Supported exploratory analysis, KPI reporting, and performance-focused data work.
05 / Notes from the Field
06 / Start a Conversation
Have a challenging problem?
Open to conversations about applied AI, computer vision, machine learning, and engineering opportunities.
Get in Touch