MATLAB Expert

Technical and commercial risk assessment for an investment project in the hardware sector (scaling potential, liability risks, operational processes) Process consulting for two SMEs in the engineering environment, with a focus on workflow digitization and PLM

• Fundamentals of electronics measurement, electropneumatic circuits & protective measures according to DIN-VDE
• Installation, wiring and commissioning of PLC-controlled Systems

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

• Led the hardware development and validation of a safety-critical 400-V battery management system (BMS) for the TOGG SUV program; performed system architecture reviews, schematic validations, EMC and reliability tests, and root cause analyses in compliance with relevant automotive standards (ECE-R10, CISPR25, ISO-26262). Coordinated cross-country EU engineering teams, customer reviews, and technical documentation to deliver a production-ready, validated system.

• Architected and integrated 22 state-of-the-art ADAS validation vehicles for BMW ADCAM and LIDAR programs at Magna Electronics; synchronized up to 26 heterogeneous sensors (LIDAR, RADAR, cameras, GNSS/INS) using PTP-based timing architectures. Defined the system architecture, HW/SW interfaces for hardware-in-the-loop (HIL) rework, sensor integration strategies, and data acquisition frameworks. This enabled scalable vehicle-level validation, improved validation efficiency by 25%, and reduced project costs by €1.45 million.

• End-to-end validation and integration of automotive radar platforms for a Daimler project at Continental. Developed automated open-loop hardware-in-the-loop (HIL) environments to accurately test target tracking KPIs, field-of-view limits, and thermal and voltage-related ECU state machines. Skilled use of a highly complex toolchain consisting of CANoe, Lauterbach Trace32, RADAR target simulators, and EMC shielding chambers for RF and system validation. Synchronized global, interdisciplinary teams to speed up troubleshooting and close critical technical gaps.

• Reconstructed and validated the product architecture of an electromechanical e-bike by integrating and troubleshooting critical subsystems (BMS, motor control, sensors, HMI, electronic locking systems). Built a comprehensive system-level test bench for functional testing, fault reproduction, and performance analysis; coordinated suppliers and implemented corrective actions to improve reliability and traceability.

• Defined the system architecture and validation strategy for a LIDAR platform developed in cooperation with Elmos Semiconductor; evaluated optical measurement concepts, SPAD detector integration, and system requirements, and created technical recommendations for product development and verification.

• Developed LabVIEW-based automation and verification software for a high-precision hydraulic and electromechanical test system for FTE Automotive; integrated NI DAQ hardware for high-frequency real-time capture of physical measurements. Developed automated test sequences, programmable endurance tests, troubleshooting routines, data logging, and analysis tools to improve test efficiency and traceability.

• Successfully delivered full engineering life cycles for well-known industrial customers (Magna, Continental, Farasis, FTE Automotive, BMW, Daimler, TOGG) – from requirements engineering and proof of concept to system integration and validation, technical documentation, supplier coordination, and user training.

A nationwide service provider for occupational medicine, occupational safety and health management, focusing on prevention, health and digital transformation.

• Role: Responsible for establishing and further developing professional project portfolio management in the Digital Transformation & Data area. Managing and supporting strategic IT and transformation projects, as well as supporting the introduction and establishment of project management standards, methods and governance structures.

Key areas:

• Establishment and further development of IT project portfolio management
• Analysis and structuring of business and technical requirements
• Management and coordination of cross-functional IT initiatives
• Stakeholder management at department, management and service provider level
• Introduction and further development of project management methods and tools (including Jira, Confluence, MS Planner)
• Preparation of decision-making documents, target visions and implementation concepts in the context of digital transformation
• Management and coordination of transformation, digitalization and organizational projects
• Preparation of decision templates and management documents for executive committees
• Conducting workshops and stakeholder management with departments and IT teams
• Definition of requirements for dashboards, reporting and KPI management
• Participation in the Wissenskompass project (requirements analysis and MVP concept)
• Analysis and evaluation of AI support opportunities in Knowledge Management
• Investigation of AI use cases to improve knowledge provision and use
• Consideration of AI requirements as part of the Wissenskompass concept
• Coordination with strategic departments to further develop project management standards
• Introduction, configuration and training for Microsoft Planner to support project and task management
• Conducting user training and supporting employees in the introduction of new ways of working and tools

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

Independent, part-time open-source engineering focused on build-time tooling for Next.js, React, MDX, and JavaScript/TypeScript compiler pipelines.

• Built next-slug-splitter to optimize content-driven Next.js applications. It analyzes MDX content at build time, resolves component usage, and generates route-specific handlers so pages avoid sharing the full catch-all component bundle.

• Created supporting plugins and utilities for scoped MDX transformations, nested component dependency resolution, compile-time refinement, safe ESTree evaluation, and object-graph diffing.

• Own architecture, API design, implementation, automated testing, npm publishing, documentation, demos, and performance benchmarking.

Building blocks:

• remark-scoped-mdx: Context-aware AST transformations with nested scope isolation, typed component registries, and prop inference.

• recma-component-resolver: Dependency-graph analysis and selective component forwarding across nested MDX includes.

• recma-static-refiner: Build-time prop extraction, schema validation, derivation, and pruning.

• estree-util-to-static-value and object-graph-delta: Safe static evaluation and deterministic, cycle-safe structural diffing.

Tech Stack:

• Frameworks: TypeScript · Next.js · React · MDX

• Compiler tooling: Unified · Remark · Recma · MDAST · ESTree · ts-morph · esbuild

• Competencies: Static analysis · AST traversal and transformation · dependency graphs · code generation · schema validation · route and bundle splitting

• Tooling: Vitest · tsup · npm · performance benchmarking

• Portfolio development & customer acquisition
• Portfolio development (RAG, NLP fine-tuning, process automation with n8n) and active customer acquisition
• Positioning: GDPR-compliant, locally hosted AI solutions for SMEs

Statistical Forecasting Algorithm

• Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
• Migration from R/On-premise to Python/Snowflake
• Productionalization on Snowflake in cooperation with data engineers & DevOps

Monitoring Dashboard

• Data engineering for preparation & provisioning of necessary data/resources on Snowflake
• Development & deployment of a Streamlit dashboard in Snowflake

ML-based Probabilistic Forecasting on Vertex AI

• Development of a ML-based probabilistic forecasting algorithm from scratch
• Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI

Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow

• Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

• 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

Testing and verification of high-voltage systems

• Performed integration and system tests for control units in PHEV/EV vehicles using ECU-TEST (TraceTronic), Vector CANoe, CANalyzer, ETAS INCA, Tornado, E-Sys, and EDIABAS.
• Analyzed the interaction of high-voltage control units (including CCU, BMU, inverter, IPB, and IPF) and carried out software updates and flash processes to verify new software versions.
• Worked closely with software, system, and integration teams in an agile development environment to analyze issues and verify new software versions.

Oracle Project Management and Automation

As part of the project, the operation of an Oracle infrastructure was automated by developing and enhancing Puppet modules and migrating existing Bash scripts to a standardized configuration management solution.

The aim of the project was to standardize and automate administrative processes, improve the maintainability of the Oracle platform, and increase the efficiency and traceability of infrastructure management through the use of Infrastructure as Code.

• Analysis and migration of existing Bash shell scripts to a Puppet-based automation solution
• Development and enhancement of Puppet modules for Oracle environments
• Automation of installation, configuration and operational processes using Infrastructure as Code
• Standardization and optimization of configuration management for Oracle systems
• Creation and maintenance of technical and operational documentation
• Planning, coordination and professional support during project implementation
• Operation and automation of Oracle systems on x86-based Linux server platforms
• Testing and quality assurance of the developed Puppet modules
• Analysis and resolution of technical errors as part of the automation and migration
• Close coordination with specialist departments and infrastructure teams to implement automation requirements

Technologies used: Puppet 7/8, SLES 15, Oracle 19, Oracle Grid, SVN, Windows Server, Confluence, VS Code, Ruby, Bash, Pass