Image Processing Experts in Germany

• Development of scalable backend applications with C#/.NET and Java
• Design and implementation of distributed microservice architectures
• Development and integration of REST APIs for industrial applications
• Development of modern web applications with React, Angular, and TypeScript
• Implementation of MQTT-based communication solutions
• Integration of industrial protocols such as OPC UA and Modbus TCP
• Development of batch, process control, and HMI components
• Containerization and deployment of applications with Docker
• Conducting code reviews and supporting architecture decisions
• Close collaboration with Product Owners, QA, and interdisciplinary teams
• Analysis of business requirements and implementation of technical solutions
• Further development of existing software architectures with a focus on maintainability and performance

Technologies: C#, .NET, ASP.NET Core, Java, C++, React, Angular, TypeScript, Vue.JS, MQTT, OPC UA, Modbus TCP, Docker, GitLab, MariaDB, MySQL, Linux

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

• Conclusion of partner agreements with OnePlan (portfolio management) and ForProject/EVMS (project controlling); ongoing qualification and implementation of both tools.
• Project management for the relaunch of [link] with a new focus on integrated PM/PPM toolchains.
• Development and implementation of approx. 60 PM standard methods (PMSP, CMSP, QMSP) according to ICB4 & PMBoK8 as a database application on MS Power Automate / M365 / MS Project, with a planned cloud launch in 2025.
• Deployment and cut-over standard process defined as Quality Gate QG07: go-live plan, cut-over plan, communication package, production launch, smoke test in production, hypercare team, incident and SLA management, confirmation of production stability and operational handover.
• Development of a classification and retrieval system (TOC) for ChatGPT projects to enable immediate cross-application object activation.

Tools: LinkedIn, Claude, ChatGPT, Gemini, Infinity, MS Copilot, OnePlan, EVMS/ForProject, Power Automate, SQL DB, O365, MS Project, MS Cloud, ICB4, PMBoK7, M365, Teams

• Coordinate a large-scale, DFG-funded research project involving 19 interdisciplinary research groups across multiple institutions, including the MibiNext Graduate School
• Lead project governance and stakeholder communication through structured milestone tracking and reporting to university administration
• Organise and coordinate scientific events, including an international symposium and DFG on-site evaluation, managed invitations, participant communication and event-related enquiries
• Website maintenance and content creation
• Editing, formatting, reviewing, and compilation of abstract book and various documents
• Coordinate logistics, communication, scheduling, and stakeholder engagement across participating institutions

• Portfolio development & customer acquisition
• Portfolio development (RAG, NLP fine-tuning, process automation with n8n) and active customer acquisition
• Positioning: GDPR-compliant, locally hosted AI solutions for SMEs

• Role: Simulation Engineer
• Environment: Mechanical Engineering / Visual Inspection
• Goals & Implementation: Simulation of photometric-stereo setups to determine the best positions for cameras and light sources for each specific part.
• Business Value: Enabled a low-cost and scalable solution for determining part-specific hardware setups.
• Tech Stack: Python, Blender

In this project, I was responsible for designing and implementing a new, modular, and highly scalable test bench platform for an automotive supplier. My focus was on developing a robust system architecture and programming the associated software, with UML modeling used consistently throughout. This structured approach significantly increased the reusability of individual LabVIEW test modules and sustainably reduced development time for subsequent projects. I also integrated modern automotive standards such as CAN FD, LIN, and safety requirements, making the solution future-proof.

Technologies: LabVIEW, Visio, UML, CAN FD, LIN, DMM, Safety, Image processing, Project management, HMI, C#, C++, Python, Rust, CI

• Fine-tuned and evaluated alternative backbone architectures for the AFFGAN handwriting style encoder to better capture writer-specific features, benchmarking against the original baseline architecture and replacing the legacy VGG19 architecture with a custom Transformer-based encoder.
• Extended the system beyond word-level synthesis to line-level handwriting generation, integrating and adapting an existing line-generation architecture to support full-sentence output.
• Deployed the handwriting generation pipeline as a web application, enabling users to select a writing style, input custom text, and instantly generate a corresponding handwriting image.
• Integrated TrOCR into a teacher–student distillation framework to evaluate and improve readability of generated handwriting, improving OCR accuracy by 20% over the baseline.
• Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.

Tech: PyTorch, Python, GANs, CNN, ViT, TrOCR, Streamlit, CUDA.

• Adding/creating test specifications
• Performing regression tests
• Performing release tests
• Analyzing Jira tickets
• Identifying software defects and fixing them with C#

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

• Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

• Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

• Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

• Developed and implemented AI-based perception and multimodal systems for real-world environments
• Built, trained, and evaluated Machine Learning and Deep Learning models using Python, PyTorch, TensorFlow, and OpenCV
• Worked with Vision-Language Models (VLMs), Large Language Models (LLMs), transformer-based architectures, and multimodal AI systems
• Applied LoRA-based fine-tuning techniques and experimented with diffusion models for generative and multimodal AI applications
• Developed multimodal perception pipelines using camera, LiDAR, and sensor data
• Designed end-to-end workflows for data processing, model training, evaluation, benchmarking, and robustness analysis
• Utilized HuggingFace Transformers and modern Deep Learning frameworks for AI experimentation and deployment workflows
• Applied GPU-accelerated computing, CUDA-based processing, ONNX, and TensorRT optimization for efficient inference and large-scale model training
• Collaborated with industry partners including Valeo and REHAU on applied AI and intelligent system projects
• Developed scalable AI architectures and prototype software solutions for automation and perception tasks

• Consulting and development of suitable communication concepts between S7-300 and S7-1500, taking the existing PROFIBUS infrastructure into account
• Technical support for connecting the S7-300 controllers to the existing recipe data block of the S7-1500
• Assisting in the development of the communication sequence for recipe access (read, write, acknowledge, status handling)
• Configuration of S7 communication (PUT/GET)
• Creation and adaptation of WinCC V7 scripts (C scripts and VBS scripts) for WinCC-based recipe management
• Development, optimization, and integration of recipe processes into the existing WinCC project structure
• Commissioning of the new communication interfaces and recommissioning of the recipe logic on existing systems
• Creation of technical documents: communication sequences, functional descriptions, commissioning documentation

Development of an iOS app for virtual try-on and outfit suggestions

The goal of the project is to develop a mobile application for personalized, photorealistic outfit suggestions. Users should be able to upload their own photos, try on clothes virtually, and find products that can be bought directly in the generated suggestions.

• Planning and implementation of the onboarding and photo upload in the iOS app
• Development of the mobile application with Expo and React Native
• Implementation of a Hono/Node.js backend for user, product, and generation processes
• Building an asynchronous processing pipeline with BullMQ and Redis
• Connection of PostgreSQL/pgvector and S3 for product, image, and generation data
• Integration of Gemini and OpenAI for outfit generation and image processing
• Implementation of a credit system and integration of RevenueCat
• Integration of Stripe Connect and affiliate product feeds for products that can be bought directly

Label: TypeScript, React Native, Expo, Hono, Node.js, PostgreSQL/pgvector, BullMQ, Redis, S3, Gemini, OpenAI, RevenueCat, Stripe Connect, Docker