Radar Experts in Germany

• Development of scalable backend applications with C#/.NET and Java
• Design and implementation of distributed microservice architectures
• Development and integration of REST APIs for industrial applications
• Development of modern web applications with React, Angular, and TypeScript
• Implementation of MQTT-based communication solutions
• Integration of industrial protocols such as OPC UA and Modbus TCP
• Development of batch, process control, and HMI components
• Containerization and deployment of applications with Docker
• Conducting code reviews and supporting architecture decisions
• Close collaboration with product owners, QA, and interdisciplinary teams
• Analysis of business requirements and implementation of technical solutions
• Further development of existing software architectures with a focus on maintainability and performance

Technologies: C#, .NET, ASP.NET Core, Java, C++, React, Angular, TypeScript, Vue.JS, MQTT, OPC UA, Modbus TCP, Docker, GitLab, MariaDB, MySQL, Linux

• Building strong awareness within the company around nearshoring
• Engagement Manager in the Nearshore Competence Center
• Responsible for nearshore consulting, partner screening and technical onboarding, designing and implementing cooperation scenarios, change management, stakeholder management, participation in steering committees, and collaboration with IT and business units
• Tools used: Microsoft Office 365, Microsoft Teams, Azure Devops, Microsoft Sharepoint, Conceptboard
• Key results: Establishment of a nearshoring strategy, successful identification and implementation of outsourcing partnerships, delivery of change management and stakeholder management at the highest level

• Responsibility for the further development of digital service products with a focus on customer value, profitability, and scalable growth
• Development and prioritization of the service product roadmap based on customer feedback, market analysis, financial evaluation, and technical feasibility
• Identification of new digital service opportunities as well as derivation of product requirements, feature priorities, and value propositions
• Alignment of product vision, roadmap, and user experience with product owners, center of competence, sales, service, and market organizations
• Support of go-to-market activities, market validations, and internal training for the successful launch of digital service offerings
• Use of data-based decision-making to assess customer needs, business model assumptions, and product progress

• Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
• Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
• Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
• Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
• Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
• Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
• Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.

• Developed a real-time video analytics proof-of-concept for deployment on NVIDIA Jetson edge devices.
• Implemented DeepStream pipelines including object detection, tracking, human pose estimation, face anonymization, and zone intrusion detection.
• Built a Qt/Python demonstration UI interfacing with the AI pipeline via REST APIs.

• Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
• Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
• Automated extraction and training processes with CI/CD
• Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
• Developed and optimized embedded software for automotive control units
• Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
• Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
• Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
• Developed and trained machine learning models using PyTorch
• Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
• Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
• Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
• Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
• Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)

Project: Intelligent Light Communication Software — Audi

• Analysis and clarification of system requirements with the customer and translation into software architecture and design decisions
• Architecture and implementation of CAN FD and SPI communication on NXP S32K312
• Design and development of flash bootloader, flash driver, and boot manager for secure OTA-capable updates
• Configuration of the AUTOSAR BSW architecture (Dcm, Dem, NvM, BswM, Os, Mcu, Port, Com stack, Rtc) for portability and maintainability
• Design of the crypto stack (Csm, CryIf, Crypto) for secure flash and diagnostic data; integration of the NXP HSE core and wrapper layer
• Integration of customer-specific modules (Sfd, Ivd, Npm) into the Vector AUTOSAR stack
• Definition of the memory architecture: Memmap, CMake, and linker script configuration
• Leading board bring-up for the Audi light demo platform; implementation of diagnostic routines and identifiers

• Engaged by MILLA Group (autonomous shuttle manufacturer) to integrate and harden a safety-critical AD stack toward production: audited the architecture across perception, HD mapping, and positioning, and delivered a gap analysis with remediation roadmap.
• Lead root-cause analysis of sensor failures across a deployed shuttle fleet; shipped remediation in a versioned AD release and drove vehicle-level field validation at multiple operational sites.
• Design and implement interfaces between perception, localization, and vehicle systems in C++; identify integration risks and drive resolution of cross-subsystem technical issues across the AD stack.
• Standardized the client's software development lifecycle by introducing Agile workflows and CI/CD pipelines, shortening integration and validation cycles.

• Topic: Object Detection and Semantic Segmentation for (AUV) Systems using Transformer-Based Vision Models and Sensor Fusion.
• Designed and implemented an end-to-end multi-sensor fusion perception pipeline (Camera, LiDAR, IMU) in ROS
• Developed CNN-based Machine Learning model (YOLOv8) and Transformer-based vision models for real-time object detection
• Processed and clustered 3D LiDAR point clouds using DBSCAN, RANSAC, and voxel grid filtering to enable robust object localisation in noisy environments.
• Designed Bayesian Network models (GeNle) for probabilistic reasoning and sensor-level decision fusion under uncertainty.
• Applied Kalman filtering for sensor state estimation, temporal alignment, and smooth object tracking, reducing false positives in safety-critical scenarios.
• Evaluated system performance under realistic driving dynamics, improving tracking stability and overall perception robustness.
• Built deep learning pipelines for training, validation, and performance evaluation of perception models using sensor data.

• Development and maintenance of hardware drivers in C++
• Conducting unit and integration tests to ensure code quality
• Debugging and fixing issues with the hardware team and FPGA team
• Defining and developing software concepts and coordinating with the software architect
• Expanding test automation to improve efficiency
• Research and development of algorithms to improve existing codebases (runtime, memory usage, accuracy)

Seqnaut and Sycosm (WIP) – Embedded Audio DSP & Synthesis Platform

• Embedded audio DSP framework — synthesizers, effects, drum machines; modular audio graph
• Sequencing: PianoRoll, StepGrid, MasterClock; 8-voice polyphonic synth; CLI + OSC
• Transient Detector — dual-envelope adaptive threshold, guitar attack detection
• Hardware: Teensy 4.1, custom AFE design tapping BOSS GX-100; MIDI FX integration

Label: C++23, C, Python, CMake, FreeRTOS, Teensy 4.1 (ARM Cortex-M7), Teensy Audio (Stoffregen), DaisySP, I2S/SAI, OSC, MIDI

Stellar SR6 G7 line, 32-bit Arm® Cortex®-R52+ MCU.

• MISRA-C, C99, Greenhills ARM compiler
• Dassault AUTOSAR Builder
• EB Tresos
• Sparx Enterprise Architect 16.1
• VS Code
• Python xml, lxml, NumPy and Pandas

ISO 26262, ISO 21434, hypervisor, key management, HSM. Tooling & automation. LieberLieber LemonTree + Sparx EA. Jira, Confluence, SharePoint. Git/Github. DevOps through Jenkins & Conan.

• Creating structured item definitions as the basis for conducting FMEAs

• Restructuring/optimizing existing FMEAs according to the VDA/AIAG handbook in PeakAvenue/Plato. Focus on the quality of function and failure-mode definitions

• Adding functions and failure modes

• Revising the risk analysis. Adding and rating prevention and detection measures

• Developing a framework for improved tracking of measures

• Team training

• Improving FMEA, with a focus on early failure prevention / Early Failure Mode Avoidance

• Successfully conducting customer reviews