Calibration Experts in Munich

• Development of new simulator features and extension of existing modules using C++.

• GUI design and implementation with Qt for Windows, including redesigns to improve usability and workflow.

• Maintenance and expansion of existing codebase: refactoring, bug fixes, and modularization to support new capabilities.

• Version control using Git.

• Performance optimization of simulation to meet real-time constraints.

• Creating and maintaining documentation: technical comments, design docs, API references and user manuals.

• Led a redesign that improved operator workflow and reduced task completion time.

• Extended simulator architecture to support multiple new scenario types while preserving backward compatibility.

• Implemented refactoring and modularization that simplified maintenance and accelerated feature delivery.

• Technologies: C++03, Qt4.6, QMake, Git, V-Model, Windows

AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.

• Project Title: Path Planning Module Development (Oct 2024 – Jun 2025)

• Developed path planning module using C++14 and CMake

• Implemented gRPC communication protocol between modules

• Performed unit testing using Pytest framework and Python

• Project Title: HMI Programming for Battery, Fuel Cell Electric Vehicle (Aug 2023 – Sep 2024)

• Developed HMI software for BEV/FCEV using Ruby and Crystal for backend

• Implemented frontend using Vue.js framework

• Conducted bug fixes and simulator testing

• Project Title: IFHOST CAN Bus Programming (Jan 2023 – Jul 2023)

• Programmed CAN bus software using C and C++

• Executed unit tests with Google Test framework

• Performed integration testing using CAPL in Vector CANalyzer

• Participated in onsite testing

• Architected an agentic, real-time offer orchestration engine where specialized agents (retrieval, pricing/optimization, and policy/guardrails) coordinate to personalise promotions across customer touchpoints using RAG with FAISS over Delta Lake and low-latency Databricks Model Serving. Collaborated with product managers and commercial stakeholders to shape the roadmap and evaluate emerging agent patterns for production.

• Designed an agent-based data quality service that orchestrates schema detection, entity normalization, and validator/exception-handling agents to clean multi-retailer SKU feeds at scale. Wrapped model calls in PySpark UDFs for distributed inference, automated via Databricks Workflows and CI/CD.

• Developed a multimodal, agentic extraction pipeline where vision, parsing, and compliance agents collaborate to derive brand, packaging, and volume from scanned images using Claude 3 Sonnet with Swin Transformer encoders. Orchestrated via Azure Event Hub with outputs persisted to Delta Lake.

• Implemented a GS1 taxonomy classification service built around cooperating agents for inference, drift monitoring, and auto-retraining governance using Falcon 180B (LoRA-tuned) with a batch pipeline on Databricks.

• Created a hybrid agent workflow where a retrieval agent surfaces candidate matches via embeddings and a reasoning/verification agent (Mixtral 8x7B) adjudicates receipt-to-SKU alignment, integrated into a streaming Databricks pipeline.

• Built a multimodal attribute inference pipeline structured as cooperating vision-language, rules/consistency, and compliance agents to fill NutriScore, nutrition fields, and packaging types from names and images using LLaMA 3-8B with CLIP embeddings.

• Developed a GenAI-powered orchestration system that ingests recipes from multiple websites, parses ingredients through structured extraction agents, and dynamically links them to real-time retailer offers via tagging, semantic reasoning, and business-rule agents.

• Project: Sensor fusion application for traffic participant detection
• Software frameworks: C++ (11,14), Python, Visual Studio, MTS, Qt, GitHub, Jenkins, JIRA, Confluence, Conan, CAN, RTOS, DOORS, CMake
• Refactored and adapted sensor fusion algorithms by processing sensor data (camera and radar) for ACC and EBA as per requirements
• Handled system test issue reports in JIRA
• Tuned Kalman filters and introduced new features to enhance tracking
• Adapted architecture, detailed design (UML) and simulation tool (Qt)
• Conducted unit testing, code reviews and static code analysis in compliance with MISRA standards
• Conducted regression testing to validate software, involved in software releases (CI/CD), KPI evaluation by testing NCAP scenarios
• Flashed ADAS software to target vehicles, used UDS protocol and OBD-II tools for diagnostics and verification

• Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
• Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
• Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
• Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
• Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.