Git Experts in Nuremberg

Embedded Analytics & AI-assisted BI

Design and development of an integrated analytics solution based on ASP.NET Core, Power BI Embedded, and LLM services to provide contextual business information.

Development of an AI agent with Function/Tool Calling for secure orchestration of REST APIs, SQL data sources, and technical services within defined business processes.

Build-up of automated BI workflows including workspace management, deployment processes, and scheduled refresh via the Power BI REST API.

Implementation of secure service-to-service communication with Microsoft Entra ID and service principal, as well as integration into existing enterprise system landscapes.

Technologies: ASP.NET Core, C#/.NET, Power BI Embedded, Power BI REST API, LLM API, AI Agents, Function/Tool Calling, Entra ID

• Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
• 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.
• Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment

• Designing and developing responsive, accessible web applications for SMEs and private clients with React.js, Tailwind CSS, JavaScript, and HTML5.
• Building reusable UI component libraries to improve maintainability and reduce development time.
• Integrating REST APIs and implementing dynamic features (landing pages, portfolios, dashboards) based on Figma/Adobe XD designs.
• Independently managing the full project cycle (analysis, development, testing, deployment on Netlify/Vercel) while following mobile-first and cross-browser standards.

• Helped to design the AWS infrastructure, integrated services and backend architecture for use cases of an on-premise solution and partial migrations to AWS with fast response times
• Implemented automated AWS integration test suites
• Implemented mission-critical components and delivered them before the deadline in a production-ready state with operation and monitoring concepts
• This 2-month subproject was about building a data-intense pipeline (5 TB) to be enriched continuously with data
• Designed and implemented reusable AWS CDK constructs to be used across the company’s teams to enable faster onboarding with AWS
• Coached on AWS topics, distributed software patterns, security, domain-driven design, agile collaboration and documentation to improve performance and collaboration
• Technologies: AWS, GitHub Actions, ETL, monitoring, operations, TypeScript, Python, AWS CDK, CloudFormation, Java, Docker, AWS ECS, AWS Lambda, serverless, Jenkins, DevOps principles

• Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
• Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.

• Technology update
• Microsoft Azure certification

• Extension of test software for functional testing of IO modules in WPF with MVVM
• Configuration and extension of a specialized WPF control from Nevron
• Connection to Siemens PLCs via Siemens OPC UA Client and Allen-Bradley/Rockwell PLCs via Codebix OPC UA Client
• Integration of various data sources via REST API and JSON
• Development of stored procedures, views, and triggers for PostgreSQL and SQL Server with ADO.NET and Entity Framework 6

• Gathered tire images and their feature descriptions.
• Cleaned dataset of image metadata using pandas.
• Stored image feature embeddings in Chroma vector db.
• Used image augmentations to increase dataset size.
• Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
• Used PyTorch to train and test different neural networks.
• Validated model using custom accuracy metric based on similarity search in ChromaDB.
• Visualized accuracy predictions using matplotlib.
• Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
• Created custom Docker image in Amazon Elastic Container Registry for machine learning script.

• Intent-driven approach to configure all models across all series automotive in all distribution markets of this car manufacturer
• Developed a customer-facing, conversation-driven integration layer to achieve 100% hallucination-free technical configurations
• Utilized Microsoft Azure AI Services: AI Foundry, Agent Service, AI Search; Prompt Shield Services; Content Security; Terraform; API Gateway; AI Gateway; Container Services; Azure Agent SDK; Agent Skills; RAG; MCP Servers and tools

• Built features using Jetpack Compose
• Improved location-based features with in-memory caching
• Developed Merchant-to-Merchant chat functionality from scratch
• Integrated FCM with deep links to enable contextual push notifications
• Performance tuning for Google Maps view using clustering for better performance in dense areas

• Engineered high-performance APIs with FastAPI + MongoDB, integrating live weather data (NOAA, NWS).
• Developed an AI chatbot with OpenAI APIs — context-aware by location, profession & interests.
• Created admin dashboard APIs for real-time monitoring and zero-downtime configuration.
• Integrated Stripe Embedded Payments with secure transactions & subscription management via webhooks.

• Analyzed 50K+ customer records using SQL and Python in a cloud services firm, identifying trends
• Designed interactive Tableau dashboards for sales and marketing stakeholders, reducing report
• Developed ARIMA and AutoARIMA time series models to forecast AWS resource utilization, cutting
• Automated ETL pipelines with Python, improving workflow efficiency by 20% for scalable data
• Collaborated with DevOps teams to deploy 3 machine learning models in production using Docker

• Tribe Home, Product Area 4 - Customer in Life, Squad Cybertron.
• Analysis, design, development, and testing of new requirements for Optimmo, mortgage financing software.
• Analysis, design, development, and testing of the JEE application MWS kredit-baufi.
• Analysis, design, development, and testing of the Wicket Optimmo application.
• Analysis, design, development, and testing of kredit-baufi batch programs.
• Migration of kredit-baufi batches to RHEL9.
• CiL SCS consumer pacts with Finagle.
• CiL SCS touch point architecture integration.
• Team size: 10.
• Tools / Frameworks: OpenJDK Java 17, Kotlin 1.6.0, Azure, Jenkins, Stash, JEE, GitLab, JIRA, Confluence, git, Spring 6.0, Spring Cloud, Spring Data, Hibernate, JPA, JMS, Kafka, Oracle, PL/SQL, Red Hat Enterprise Linux (RHEL), Maven, REST, JBoss, IntelliJ IDEA, Wicket 9 and 10, Istio.

• Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
• Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
• Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS