PyQt Experts in Germany

• Built a Trend-Scouting AI system to automate technology intelligence in power electronics and semiconductors, combining Azure OpenAI with LangChain, Scrapy-based web crawling for structured, noise-free data acquisition, and automated PDF reporting for internal R&D use. Developed a FastAPI-based (Uvicorn) web application to validate LLM outputs, test prompt strategies, and enable interactive system evaluation.
• Developed a real-time STM32 binary telemetry debugger with a PyQt-based GUI, featuring header-based frame synchronization, anomaly detection, template-driven payload decoding, time-aligned buffering, and live signal visualization.
• Developed an AI-driven power inductor designer using surrogate regression models for accurate electromagnetic and thermal prediction. Integrated multi-objective NSGA-II optimization to generate efficient, manufacturable designs.

• Development of Python applications for data extraction and filtering using Tkinter and Bitbucket
• Digitization of a manual process, making workflows approximately 20% faster and more efficient
• Collaboration with specialist departments to implement process automation projects

• Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).

• Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.

• Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.

• CMP also includes the complete hardware management lifecycle.

• Kubernetes, OpenStack and Hadoop are used as the foundation.

• The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.

• Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.

• Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.

• OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.

• Development of a Java application Rudi: SOAP, REST, containers, DB.

• Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).

• Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
• Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
• Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
• Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
• Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)

• Applied pose estimation frameworks on thermal images using infrared cameras to enhance temperature analysis and thermal comfort evaluation.
• Performed CFD simulations to analyze and visualize airflow and temperature distribution in enclosed spaces, providing data-driven insights for improving HVAC system efficiency.
• Used transfer learning to adapt pre-trained deep learning models for different tasks and datasets, improving accuracy and reducing training time.
• Handled large datasets and applied visualization techniques such as boxplots, scatter plots, and other graphical tools to identify trends, detect anomalies, and validate data accuracy.
• Built machine learning predictive models such as linear regression, logistic regression, and classification models.
• Containerized ML models and data pipelines with Docker and orchestrated scalable training and inference workflows using Kubernetes.

• ROS/ROS2
• Programming Language: Python, C++

Redesign and implementation using Python.

• Technologies: Python 2.7, QGIS 2.6, Qt, MapInfo/MapBasic

• Current Grade: 1.4.
• Applied Programming on Signal Processing: Fourier Transform, Filtering, VisPy, and NumPy.
• FAUST WebSecurity Workshop.
• Hands-on experience in LLMs, Applied Data Science, and data analysis.
• Proficient in Python, PyQt5, C++, MS Office, Git, Pandas, NumPy, VisPy, JavaScript, CSS, Machine Learning, and HTML.