Qt Experts

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

• Analysis of the existing CAN communication between microcontrollers
• Analysis of the sensors used and the measured values collected
• Planning the CAN messages for transmitting the measured values
• Iterative adjustment of the microcontroller code to the new CAN messages
• Cross-compilation from x64 to arm64

• Introduction of 5 different process applications for different teams

• Release planning: scope and time management

• Resource/capacity planning

• Conducting Sprint Planning / retrospectives

• Increment planning (several sprints)

• Preparation of Steering Committee meetings / reporting to the Board

• Coordination / alignment with external suppliers / deliveries

• Resource planning for multiple projects

• Coordination with business department and development team

• Identification of best practices with IBM BAW

• Cost control and planning for the project team and external service providers

• Collection of KPIs using LogScale

• Analysis of application errors with LogScale / Queries

• User Story definitions / coordination of requirements with the business department and development team

• Testing and defect tracking

• UI/UX design of applications

• Preparation and moderation of Brown-Paper workshops

• Test concept, test data, test organization, test execution

• Collection of team velocity / metrics

• Test execution

• Script for automated testing

• Test organization with business department and IT

• Recording and prioritization of defects

• Establishment and operation of the application

• Establishment of application monitoring with LogScale Dashboards

• Checking health endpoints with PowerShell

• Post Mortem analysis

• Establishment of Incident Management

• Establishment of Problem Management

• Analysis of errors using LogScale Queries and Dashboard

• Pre-processing of data for AI

• Evaluation using AI language models (Meta Llama 3.3 LLM and deepset Haystack) and RAG

• Installation of runtime environments for LLMs (Large Language Model)

• Evaluation of various LLMs

• Installation of RAG (Retrieval Augmented Generation) and integration with LLM

• Extraction of unstructured data with LLM and RAG

• Project based on IBM BAW (Business Automation Workflow), Websphere Liberty, Domea, d.3, REST, LogScale (formerly Humio), Swagger, Powershell, JIRA, Confluence, Lucom Interaction Platform (LIP), Mattermost, Jabber

Independent, part-time open-source engineering focused on build-time tooling for Next.js, React, MDX, and JavaScript/TypeScript compiler pipelines.

• Built next-slug-splitter to optimize content-driven Next.js applications. It analyzes MDX content at build time, resolves component usage, and generates route-specific handlers so pages avoid sharing the full catch-all component bundle.

• Created supporting plugins and utilities for scoped MDX transformations, nested component dependency resolution, compile-time refinement, safe ESTree evaluation, and object-graph diffing.

• Own architecture, API design, implementation, automated testing, npm publishing, documentation, demos, and performance benchmarking.

Building blocks:

• remark-scoped-mdx: Context-aware AST transformations with nested scope isolation, typed component registries, and prop inference.

• recma-component-resolver: Dependency-graph analysis and selective component forwarding across nested MDX includes.

• recma-static-refiner: Build-time prop extraction, schema validation, derivation, and pruning.

• estree-util-to-static-value and object-graph-delta: Safe static evaluation and deterministic, cycle-safe structural diffing.

Tech Stack:

• Frameworks: TypeScript · Next.js · React · MDX

• Compiler tooling: Unified · Remark · Recma · MDAST · ESTree · ts-morph · esbuild

• Competencies: Static analysis · AST traversal and transformation · dependency graphs · code generation · schema validation · route and bundle splitting

• Tooling: Vitest · tsup · npm · performance benchmarking

• 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

Testing and verification of high-voltage systems

• Performed integration and system tests for control units in PHEV/EV vehicles using ECU-TEST (TraceTronic), Vector CANoe, CANalyzer, ETAS INCA, Tornado, E-Sys, and EDIABAS.
• Analyzed the interaction of high-voltage control units (including CCU, BMU, inverter, IPB, and IPF) and carried out software updates and flash processes to verify new software versions.
• Worked closely with software, system, and integration teams in an agile development environment to analyze issues and verify new software versions.

• Own the technical direction of several AI products in a product-owner capacity: shape roadmaps, guide the in-house development team, and provide oversight from concept through delivery.
• Act as the technical bridge between stakeholders, end clients, and vendors for a solutions provider (reseller and in-house products): evaluate vendor solutions against client needs, lead vendor discussions, and support sales and pre-sales with technical solution design.

• Own the software system architecture for laboratory automation products; specify interfaces across software, middleware, hardware and motor control in a regulated IVD environment.
• Led architecture evaluations and proof-of-concepts for integrating AI capabilities (anomaly detection, predictive maintenance) into lab automation under medical-device quality standards.
• Introduced GenAI-assisted development tools across the team, improving productivity and code review quality.
• Communicate architecture decisions to product and project management; coordinate research and improvement projects with system, electronics and external partners.

Oracle Project Management and Automation

As part of the project, the operation of an Oracle infrastructure was automated by developing and enhancing Puppet modules and migrating existing Bash scripts to a standardized configuration management solution.

The aim of the project was to standardize and automate administrative processes, improve the maintainability of the Oracle platform, and increase the efficiency and traceability of infrastructure management through the use of Infrastructure as Code.

• Analysis and migration of existing Bash shell scripts to a Puppet-based automation solution
• Development and enhancement of Puppet modules for Oracle environments
• Automation of installation, configuration and operational processes using Infrastructure as Code
• Standardization and optimization of configuration management for Oracle systems
• Creation and maintenance of technical and operational documentation
• Planning, coordination and professional support during project implementation
• Operation and automation of Oracle systems on x86-based Linux server platforms
• Testing and quality assurance of the developed Puppet modules
• Analysis and resolution of technical errors as part of the automation and migration
• Close coordination with specialist departments and infrastructure teams to implement automation requirements

Technologies used: Puppet 7/8, SLES 15, Oracle 19, Oracle Grid, SVN, Windows Server, Confluence, VS Code, Ruby, Bash, Pass

• Further development and operation of a central backend service for providing vehicle inventory for international Mercedes-Benz online shops
• Further development of a reservation service for vehicles as part of the checkout process
• Design and implementation of a highly scalable end-to-end test architecture with a focus on maintainability, reusability, and a high number of automated test cases
• Development of a multi-layer test infrastructure with a strict separation of test logic and access layers
• Development of an initialization and caching architecture to significantly speed up local and CI/CD-based test runs
• Development of an AI-supported review architecture for automated evaluation and quality assurance of end-to-end tests
• Development of a dashboard for consolidated display of a vehicle context across multiple backend systems, including AI-generated summaries and compact case analyses
• Integration and further development of connections to various reservation systems via Apache Kafka and REST
• Design of new microservices and support with architecture decisions
• Risk analysis and design of microservice migrations
• Technologies: Java, Spring, Spring Boot, Gradle, Maven, Apache Kafka, REST, OpenAPI, Swagger, Github, Confluence, CI/CD, Microservices, AI, LLMs

• Developed model-based software for hydrogen tank control units using MATLAB/Simulink, aligned with ISO 26262 functional safety requirements
• Performed system integration and software verification across MIL and HIL environments using dSPACE and Vector tools
• Defined software architecture (SADS) with interface specifications for CAN, analog, and digital communication protocols
• Generated production-ready embedded C code using Simulink Embedded Coder; created and maintained A2L calibration files with CANape
• Designed and executed MIL/HIL test cases ensuring full coverage of functional and safety requirements
• Implemented CODESYS PLC applications on IFM ecomatController; applied CAN and Modbus TCP/IP communication protocols
• Contributed to HMI/UI development using Qt/C++ on Linux (Qt Creator)
• Applied agile Kanban workflow with Git-based version control (Bitbucket)

Tools: MATLAB, Simulink, CANape, CANalyzer, PCAN-View, Embedded Coder, CODESYS, TTC2038/PXROS, Codebeamer, Jira, Git

• Development and maintenance of IT architectures with embedded Linux systems.
• Designing, implementing, and optimizing backend applications and script-based solutions.
• Analyzing and resolving issues, including troubleshooting and user support.
• Developing and implementing security concepts for cloud solutions.
• Administering networks (DHCP, DNS, NTP, VPN).
• Technologies: Linux, PowerShell, Bash, Python, Ansible, Kubernetes, GitLab CI.
• Methods: Kanban.

• Built a modular C++ 20 embedded Linux acquisition system on a Raspberry Pi, synchronizing IMU and dual-camera data streams to sub-millisecond accuracy.
• Integrated retro-reflective and contrast sensors to trigger acquisition and detect gaps between sorter rails.
• Implemented SPI & I2C sensor communication, GPIO interrupt handling with libgpiod, and CSV & JSON output.
• Designed a multi-threaded acquisition pipeline and an SPSC queue between acquisition and writer threads.
• Built a Python/HTML/CSS based web-server and validated the prototype in a DHL warehouse for defect detection.
• Documented software behavior, configuration, and results for maintainable handover and further development.

• Requirement-based test case design for automated parking maneuvers.
• Implementing and running test cases.
• Integration within the existing AVP (Automated Valet Parking) SW framework.