I am a multidisciplinary software engineer with over 6 years of experience in backend, frontend, embedded platforms, and AI integration. Architecting full-stack applications, and enhancing performance in data-driven envi…
I am a multidisciplinary software engineer with over 6 years of experience in backend, frontend, embedded platforms, and AI integration. Architecting full-stack applications, and enhancing performance in data-driven environments. I am passionate about leveraging technology to solve complex problems and contribute to innovative projects.
Built and deployed full-stack web applications using React, VueJS,Django, FastAPI, and Node.js, with clean architecture and modulardesignDesigned responsive, cross-platform frontends using Tailwind CSS,SCSS, and Bootstrap, optimized for performance and accessibilityDeveloped scalable backend systems with Spring Boot and MongoDB,integrating RESTful APIs and handling real-time data workflowsSet up CI/CD pipelines and managed deployments on Heroku, Vercel,and Hetzner, including server configuration with Nginx and ApacheAutomated infrastructure tasks and monitoring using Docker, GitHubActions, and shell scripting for streamlined DevOps operations
Electronics Department• Testing and debugging C libraries used by the OS9 operating system• Writing OS9 kernel drivers for USB stick management to prevent data loss
Vahle Technologiezentrum für Forschung und Entwicklung VTC• Defining and designing a desktop tool for data analysis in Python• Data cleansing using Pandas and Numpy• Data visualization with Seaborn and Matplotlib• Creating dynamic PDF reports using Python FPDF• Increased application performance by 30% through code improvements• Managing GPIOs of Raspberry Pi 4 with C
Software platform department at b-plus GmbH• Developed kernel drivers for Kontron SoC• Utilized alternative Linux build systems like Yocto and Buildroot
Automotive Süd• Backend development using Spring Boot, Spring Security, Spring Data, andSpring MVC• Contributed to projects booking app and Stickman configurator projects• Worked in an agile Scrum team, defining technologies and functionalities• Utilized Angular for frontend tasks
Enhanced MicroPython firmware on ESP32 by integrating a custom Cfunction to compute the Moore-Penrose pseudo-inverse, enablingseamless use as a Python module for efficient numerical computation.Applied core concepts of machine learning and feedforward algorithmsto build robust classification systems on microcontroller platforms.Explored the mathematical underpinnings of the Extreme LearningMachine (ELM), architecting a variant with dual hidden layers to extendmodel capacity and improve interpretability within embedded systems.Implemented the ELM feedforward algorithm in MicroPython fromscratch, replacing an existing Multilayer Perceptron (MLP) to optimizeclassification workflows in edge computing environments.Achieved a 10-second performance improvement over 100 testsamples in motor bearing classification by transitioning from MLP toELM, demonstrating superior real-time inference capabilities onresource-constrained hardware.
Elektronik & Versuchsfeld EAE• Created Petalinux with Xilinx SDK as part of Yocto project• Developed Yocto recipes and layers• Conducted research on database engines and performance profiling• Introduced to FPGA and VHDL• Developed data management tools and systems suitable for Petalinux
Developed kernel drivers for Kontron SoC
Utilized alternative Linux build systems like Yocto and Buildroot
Defining and designing a desktop tool for data analysis in Python
Data cleansing using Pandas and Numpy
Data visualization with Seaborn and Matplotlib
Creating dynamic PDF reports using Python FPDF
Increased application performance by 30% through code improvements
Managing GPIOs of Raspberry Pi 4 with C
Testing and debugging C libraries used by the OS9 operating system
Writing OS9 kernel drivers for USB stick management to prevent data loss
Automated production requests using Excel VBA
Managed production request revisions for semiconductor layers
Created Petalinux with Xilinx SDK as part of Yocto project
Developed Yocto recipes and layers
Conducted research on database engines and performance profiling
Introduced to FPGA and VHDL
Developed data management tools and systems suitable for Petalinux
Enhanced MicroPython firmware on ESP32 by integrating a custom C function to compute the Moore-Penrose pseudo-inverse, enabling seamless use as a Python module for efficient numerical computation
Applied core concepts of machine learning and feedforward algorithms to build robust classification systems on microcontroller platforms
Explored the mathematical underpinnings of the Extreme Learning Machine (ELM), architecting a variant with dual hidden layers to extend model capacity and improve interpretability within embedded systems
Implemented the ELM feedforward algorithm in MicroPython from scratch, replacing an existing Multilayer Perceptron (MLP) to optimize classification workflows in edge computing environments
Achieved a 10-second performance improvement over 100 test samples in motor bearing classification by transitioning from MLP to ELM, demonstrating superior real-time inference capabilities on resource-constrained hardware
Backend development using Spring Boot, Spring Security, Spring Data, and Spring MVC
Contributed to projects booking app and Stickman configurator projects
Worked in an agile Scrum team, defining technologies and functionalities
Utilized Angular for frontend tasks
Automated production requests using Excel VBA
Managed production request revisions for semiconductor layers
Created Petalinux with Xilinx SDK as part of Yocto project
Developed Yocto recipes and layers
Conducted research on database engines and performance profiling
Introduced to FPGA and VHDL
Developed data management tools and systems suitable for Petalinux
Enhanced MicroPython firmware on ESP32 by integrating a custom C function to compute the Moore-Penrose pseudo-inverse, enabling seamless use as a Python module for efficient numerical computation
Applied core concepts of machine learning and feedforward algorithms to build robust classification systems on microcontroller platforms
Explored the mathematical underpinnings of the Extreme Learning Machine (ELM), architecting a variant with dual hidden layers to extend model capacity and improve interpretability within embedded systems
Implemented the ELM feedforward algorithm in MicroPython from scratch, replacing an existing Multilayer Perceptron (MLP) to optimize classification workflows in edge computing environments
Achieved a 10-second performance improvement over 100 test samples in motor bearing classification by transitioning from MLP to ELM, demonstrating superior real-time inference capabilities on resource-constrained hardware
Audioly Demo project• Developed a Voice AI platform, by deploying TTSAI models: XTTS, Kokoro, ParlerTTS, MeloTTSOpenvoice, Whisperspeech Bark TTS, Tortoise TTS,Edge TTS, GTTS and STT AI models: openai-whisper via Ollama. I used Nodejs for backenddevelopment, Scss for front end and Python forAI&ML.
Project in data analysis and PDF automation using GUI___________________________________________________________________________________________________
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