Object Detection Experts in Munich

• Engaged in a structured career development program while strengthening German language proficiency (B1 level) and evaluating opportunities in ADAS/AD systems and requirements engineering.

• Development of high-performance ad distribution via auction
• Holistic (multi-campaign & multi-channel) advertisement placement optimization
• Algorithmic optimization for NP-Hard/NP-e
• Multiple Knapsack Problem with constraints
• Online estimation of parameters in stochastic environments

Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker

• Conducted manual testing for claims creation, forwarding, repair cost calculation, parts selection and logging on the Gold.dat.de platform
• Automated key processes using ASKUI, including login, claim creation, calculations and parts selection
• Simulated user interactions (e.g., drag-and-drop) with ASKUI
• Integrated automated tests into CI/CD pipelines for early error detection
• Executed over 50 test scenarios and identified critical defects
• Reduced test time by 50% through automation
• Improved platform quality and user experience

• Specialized in designing and implementing robust, secure cloud solutions for critical client infrastructure.
• Expertise in Microsoft Intune environment with a strong focus on system hardening and comprehensive policy management.
• Architected NIST and ISO/IEC 27000 compliant Mobile Device Management (MDM) infrastructure tailored for an international government defense aerospace project.
• Performed an architectural role for an offline Microsoft Endpoint Configuration Manager (MECM) environment, ensuring NIST compliance while handling complex manufacturing infrastructure.

• Showcasing the world's first fully immersive AR glasses experience in a moving car at CES 2024.
• Speaker about augmented reality at international conferences (e.g. the AR Ride Concept @ Unite 2024).
• Lead a 12-person interdisciplinary software team developing Android head-unit integrations, navigation & ADAS UI, and embedded software.
• Define technical direction, drive cross-domain architecture and integration, and mentor engineers across Android, UI/UX and embedded stacks.
• Oversee a small fleet of test vehicles for validation, tests, and data collection.

• Implementation of MVPs in fixed-line communication with a team of 3 technical product owners
• Building the product roadmap
• Defining epics with business units, breaking down into features and user stories
• Managing offshore development teams
• Providing transparency and reporting to the overall program
• Agile development using SAFe approach

• Developed an end-to-end AI pipeline for analyzing 2D exploded-view drawings using computer vision and deep learning models.
• Integrated YOLO-based object detection (Bounding Boxes, Post-Processing, Overlap Handling) for accurate part and callout detection.
• Applied the Segment Anything Model (SAM) for fine-grained segmentation and separation of individual components.
• Implemented OCR and feature extraction modules, and compared Vision Language Models (VLM) and traditional computer vision approaches in terms of accuracy, runtime, and scalability.

• Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
• Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
• Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.

• Introduced an integrated approach for structural damage detection across concrete, steel, and glass using advanced technologies such as LiDAR and thermal imaging. Highlighted cross-material interactions to enhance diagnostics and enable predictive maintenance.

• Developed an NLP-based medical note simplifier that transforms complex clinical instructions into plain, child-level English. Applied prompt engineering with Flan-T5 transformer models to extract patient-relevant actions and rephrase them into clear to-do items. Built dual Flask and Tornado backends with a printable web interface.

• 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)

• Contributing to backend system development for a digital health platform
• Developing and maintaining gRPC-based microservices using Spring Boot
• Writing and optimizing automation scripts in Python, Unix, and Windows environments
• Collaborating in a cross-functional agile team to integrate new features
• Tech stack: Java, Spring Boot, gRPC, SQL, Windows/Unix scripting, Python

• Computer vision engineer responsible for development of industrial vision solutions, beginning as a working student and transitioning to a full-time role in May 2025.
• Designed and implemented vehicle detection and counting models; integrated the pipeline into a cloud-deployed system (Azure) that delivers live analytics dashboards.
• Building an offline print quality assurance system that scans corrugated-board prints on production lines to detect and classify defects such as splashes, impurities and colour deviations, deploying the solution on Jetson edge devices.
• Collaborated with cross-functional teams while focusing on computer vision components, containerization, and deployment.

• Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
• Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
• Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
• Containerized AI agents and services using Docker for consistent local development and deployment.
• Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
• Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
• Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
• Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
• Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server