Android Studio 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

• Planned and developed a CPU monitoring application for monitoring system performance and resource utilization.
• Designed and implemented a Spring Boot backend providing a REST API for processing and exposing monitoring data.
• Developed the React frontend for presenting monitoring information in a clear and user-friendly interface.
• Integrated PostgreSQL for persistent storage and management of application data.
• Containerized the application and its services using Docker Compose.
• Automated infrastructure provisioning and deployment using Terraform on AWS.
• Structured the application as a modern, maintainable system using REST-based communication between frontend and backend.
• Designed and developed a secure, scalable CPU monitoring architecture (cpu-watcher) with a dedicated collector application that streams monitoring data to the backend, reducing direct exposure of system resources.
• Designed a secure cloud infrastructure with the database isolated within a private network and OIDC-based authentication.
• Implemented Infrastructure as Code with Terraform and integrated version-controlled CI/CD pipelines to automate testing, infrastructure changes, and application deployments.
• Designed and implemented the frontend delivery architecture using AWS CloudFront.

Stack: Spring Boot · React · PostgreSQL · REST API · Docker Compose · Terraform · AWS

• QA Lead for end-to-end quality assurance of the Sparkasse & Sparkasse Business banking apps (iOS/Android); design and implementation of the Test Strategy including functional testing, regression testing, and UAT for eID, Push Notifications, and white-label components
• Creation and maintenance of test concepts and test plans in TestRail; structured Defect Management and error analysis; stakeholder management and risk management for the early identification of critical quality risks
• Accessibility Testing according to WCAG 2.1 (iOS/Android); coordination of AI-supported test automation and optimization of test processes to shorten release cycles
• Release Management: planning, management, and coordination of all test activities; status reporting to management and stakeholders; quality assessment of releases and release recommendations in an Agile/Scrum environment
• Challenge: Highly complex build and release process across several Sparkasse institutions; close coordination with the DevOps team to stabilize the CI/CD pipeline and ensure reproducible test builds before every release.

Tools: Jira, Confluence, TestRail, Android Studio, Xcode, Firebase, Jenkins, GA4

• Success: Successful launch of the eID authentication process - quality assurance ensured for 50+ Sparkasse institutions

• Title: Enhancing Syntactic Awareness in Transformer Language Models for Hindi Dependency Parsing
• Investigated syntactic knowledge captured by transformer language models (RoBERTa, XLM-RoBERTa) for Hindi dependency parsing, a morphologically rich and low-resource language.
• Developed structure-aware model variants (Struct_Roberta_hi, Struct_XLMR) by integrating a CNN-based parser network between transformer layers, inspired by the StructFormer architecture.
• Conducted extensive error analysis including label-wise, distance-based, direction-based, sentence length-based, and LVC/Non-LVC evaluations.
• Evaluated models on downstream NLP tasks (NER, POS tagging) using the IndicXTREME benchmark.

• Overall responsibility for the project lifecycle from scope definition to completion
• Close collaboration with platform teams, IT leaders, and external service providers
• Application of SAFe principles and structured sprint work
• Creation of a migration roadmap with clear milestones
• Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
• Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
• Regular status reports and running knowledge transfer sessions

• Conducting and evaluating web analyses
• Creating and editing tags and scripts in the area of tag management as well as banners and other client-side web elements in the area of consent management
• Technologies used in development: Tealium, JavaScript, IntelliJ, Git, Android Studio, Selenium, Appium, Adobe Analytics, Power Automate, Power App

• RLHF & Model Evaluation: Evaluated, ranked, and refined Large Language Model (LLM) responses based on accuracy, reasoning quality, safety guidelines, and factual correctness.
• Data Annotation & Prompt Engineering: Created high-complexity prompts, edge-case scenarios, and gold-standard reference answers to train and fine-tune generative AI models.
• Quality Assurance & Verification: Conducted rigorous fact-checking, logical consistency validation, and multi-turn response optimization.

• Design, deploy, and manage scalable, highly available, and fault-tolerant systems on AWS.
• Develop and maintain Docker images for various applications and services.
• Manage Kubernetes clusters for container orchestration, ensuring efficient resource utilization and scalability.
• Monitor and troubleshoot system performance issues, ensuring optimal performance and uptime.
• Collaborate with development teams to understand application requirements and provide infrastructure solutions.
• Implement security best practices for infrastructure, applications, and data.
• Maintain and improve system documentation, procedures, and processes.
• Provide support and mentorship to team members on best practices for cloud infrastructure and containerization.
• Stay updated with the latest industry trends and best practices in cloud computing, containerization, and Dev Ops.

• Analysis and optimization of business processes in financial accounting
• Customizing in SAP FI (e.g. company codes, chart of accounts, payments)
• Integration with other modules such as CO, MM, SD
• Consulting on legal requirements (e.g. GoBD, UStG)
• Support with month-end and year-end closing
• Creation of test plans (system test, integration test, UAT)
• Definition of test cases (e.g. postings, payment runs, dunning runs)
• Selection of suitable test data and scenarios
• Manual testing of posting processes (e.g. customer invoice, payment run)
• Validation of automatic postings (e.g. for MM credit memo)
• Checking interfaces (e.g. to subsystems or non-SAP solutions)
• Documentation of defects in the test tool (e.g. Jira, ALM)
• Analysis and feedback to developers or the customizing team
• Retest after bug fixes
• E2E tests with SAP Solution Manager (SOLMAN)
• Use of tools such as SAP CBTA or Micro Focus UFT

• Analysis
• Consulting
• Software design
• Development
• Automation
• Testing
• Deployment
• Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
• Developed best practices for working with agentic systems and AI in practice
• Created code templates

• Technical and professional management of software and ML development; largely independent implementation of programming and guidance of the team and external project partners
• Design, creation, and preparation of training and test data sets from experimental image data and simulations
• Selection, implementation, training, validation, and testing of neural networks for image-based reconstruction and transformation
• Systematic evaluation, comparison, and optimization of various model architectures (convolutional neural networks, generative adversarial networks, autoencoders, transformers)
• Design and implementation of explainable AI analyses for model interpretability and robustness assessment (analysis of feature maps, augmentation studies, guided backpropagation)
• Presentation of the developed methods and results in project meetings and at international conferences

• Atlassian & Business expert
• Lead of 1st, 2nd and 3rd level support, process mapping, process optimization, introduction of a project standard (global user management and documentation) and development of a collaboration platform to optimize teamwork, data management, reporting, ERP transformation
• The client is a sustainability-focused chemical park and offers its employees a full service for the Atlassian product suite. In addition, the adoption of the ERP software by employees, the improvement of data quality and internal exchange or collaboration should be promoted by sharing knowledge and best practices
• Process and standards development
• Project configuration and creation of technical concepts
• Workshops with employees
• Development of scalable solutions and processes and their optimization/automation
• Coordination with department process owners and management
• Process mapping and further development
• Data migration and data mining
• Lead as Scrum Master
• Dashboard for project management with project overview / development of reporting structures

• Project: Thermomix Online Mock
• Manual execution of functional tests
• Implementation of automated end-to-end tests
• Execution of regression tests and analysis of results
• Further development of the QA website on the server using Express.js, Handlebars, and Node.js
• Further development of the test environment
• Technologies: Agile software development Scrum, Robot Framework, Python, JavaScript, Git (GitLab), VS Code, Jira, Confluence, Express.js, Handlebars, Phoenix/Elixir

• Development of a new generation of Spring Boot microservices for an archiving system on OpenShift, monitoring, security analyses

Stack: Java, Spring Boot, Kafka, Vaadin, OpenShift 4.x, Maven, Artifactory, Jenkins, Git/Github, GitHub Copilot, GitOps, CI/CD, Kargo/ArgoCD