Computer-Aided Engineering Expert in Germany

UEF 3.0 · Semantic Government Overlay (SGO) · Autonomous Systems (UAS / dual use)

• Designed: Semantic Government Overlay (SGO) – AI-guided administration without replacing existing specialist procedures. Read-only semantic layer over registers and specialist processes based on the Federal Information Management (FIM). Decision authority remains with the case worker (architecture principle).
• Developed: Reference architecture with source-backed, derived statements (Executable Ontologies OWL/RDF/SHACL). Technically guaranteed purpose limitation and no-write-path principle in specialist data – auditable, without a central data pool.
• Anchored: Regulation as a design principle: EU AI Act (high-risk obligations for public-sector AI, fundamental rights impact assessment under Art. 27), GDPR, NIS2, and administrative automation limits (§ 35a VwVfG, § 31a SGB X) as technical control points in the architecture.
• Created: Methodical tool for pilot organizations: data pipeline assessment (phase 0), compliance blueprint, and management summary as a decision-ready package for public administration.
• Specified: UEF 3.0 as a successor architecture to TOGAF – decision paper, canonical ontology, six-layer architecture, read/actuate boundary, federation registry, terminology concordance, and release delta as a closed specification status.
• Architected: AI-native mission OS for autonomous UAS and ground robotics as a tactical layer on top of a separately approved autopilot. Run-time assurance according to ASTM F3269-21 (Simplex pattern): the verified safety controller keeps authority, the AI function provides suggestions.
• Designed: Three-tier architecture – Tier 0 autopilot with 650 Hz flight control on RTOS, Tier 1 AI OS with semantic world model and multi-agent cluster, Tier 2 swarm and ground mesh. Zenoh as the primary fabric, MAVLink as the only authenticated command path (single writer). Result: graceful degradation – loss of the mission, not of the aircraft.
• Secured: Two-gate chain on the read/actuate boundary – governance gate (can-question: AI Act risk class per actuation, enforced human oversight under Art. 14, immutable log) before the RTA safety monitor (is-it-correct question: flight envelope, geofence, energy reserve) with revert to the baseline controller.
• Anchored: Dual-use architecture with common core and build-time fork instead of runtime switch. Three separate legal levels: civil variant – UAS under the EASA Basic Regulation (EU) 2018/1139 with the limited applicability under Art. 2(2) of the AI Act, ground robotics under the Machinery Regulation 2023/1230 with the full high-risk obligation chain, Cyber Resilience Act for both; unarmed carrier variant as defense material under AWG/AWV and Dual-Use Regulation 2021/821 (BAFA approval); armed variant under KrWaffKontrG. Each variant lives under exactly one dominant legal regime. Evidence base: AI BOM, SBOM, and complete data lineage.
• Analyzed: System analysis and realignment of grown engineering system landscapes. Approach concept for consolidation without migration – semantic layer over the existing sources instead of data transfer. Result: decision-ready implementation concept including an evaluation model for the target architecture.

• Implementation of several automatic milling fixtures for metal processing in the automotive industry
• Implementation of various punching and bending machines, including interlinking
• Electrical design of a production line for surface treatment of roasting ovens for the food service industry

• Cost estimation for a hydraulic system, taking into account the costs over the entire product life cycle and optimizing the technical specifications.

• Creation of complete 3D designs and design drawings
• Evaluation of requirement specifications and customer specifications
• Execution of partial steps in technical work preparation according to specifications and documents, including project support
• Contact person for internal and external customers in design projects
• Design and calculation of piping and components for cryogenic media and low-temperature applications
• Support with finite element calculations

• As part of a greenfield project, a new production plant is being built in Slovakia to produce industrial media storage for OEMs and distributors once completed.

• Procurement and industrialization of production equipment for prefabrication.

• Design, procurement, installation and integration of complete production lines based on stamping, forming and welding technology with integrated, labor-reducing processes.

• Establishment of a production environment under strategic aspects according to the latest state of the art.

• Overall and continuous communication with the entire greenfield team, adjacent departments and plant and executive management.

• Development of concepts together with key suppliers and identification of ratio potentials along manufacturing process chains using value stream analyses.

• Collection, assessment and application of process data such as throughput times, manufacturing times, setup times, wait and idle times using time and multimoment studies as well as surveys.

• Forward-looking design of production systems and processes with the goal of implementing automated and competitive process solutions as part of comprehensive supply chain management.

• Planning of production workflows, definition of manufacturing methods, design of the overall layout and workstations.

• Determination and recording of process cost rates, overhead rates, cost evaluation of production processes, machine hourly rates, depreciation as well as expected share of claims and rework costs to optimize manufacturing costs.

• Continuous optimizations of product designs for their adaptation into automation-oriented production environments until the plant's SOP.

• Multi-project management for the engineering, design, procurement, installation and process integration of all relevant trades with respect to plant engineering, lifting equipment, production equipment and QA installations.

• Ongoing establishment of an overall concept in constructive, plant engineering, logistical, IT and QA aspects.

• Led component ownership for drive-line systems in Transit projects from concept to SOP, validation and production launch
• Coordinated DV/PV testing activities (NVH, durability, thermal), analyzed results and implemented design updates with CAD and CAE teams and managed documentation through Teamcenter
• Worked cross-functionally with design, testing, purchasing, plant and quality teams under an agile project structure
• Investigated plant and field issues, conducted root cause analysis and implemented engineering changes
• Participated in supplier feasibility reviews and design freeze meetings and ensured timing and cost alignment
• Created and presented DFMEA, DVP plans, campaign prevention documents and budget reviews to global stakeholders (Ford UK & Ford US)
• Delivered cost-down initiatives with technical feasibility assessments and supplier negotiation support
• Ensured compliance with Ford and international standards (e.g. GSP, ISO, vehicle attribute targets) during development and validation phases

• Development and design of drives with support from product management
• Technical support for purchasing
• Creation of requirements and functional specifications, FMEAs, calculations and verifications with CAE, test protocols
• Communication with suppliers, customers, production, assembly, quality management and product management
• Testing products and error analysis
• Support for marketing activities (e.g. trade shows)

• Conception and detailed design of mechanical systems.

• CAD modeling, drawings and calculations.

• FEM, motion analysis and reverse engineering.

• Preparation of technical proposals and claim management.

• AIRBUS A-320 Assembly Station.

• AIRBUS A-350 Manual Assembly Station.

• FORD Truck R&D Prototype Manufacturing Station.

• Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
• Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
• Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
• Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
• Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
• Exported reusable pipelines and trained models with joblib for deployment.

• Preparation of data for creating masks for the manufacture of semiconductor chips.

• Architecting and developing SaaS platform for graphical definition and execution of pandas data processing pipelines
• Developed POlyglott, an open-source Python CLI tool for translation workflow management featuring PO file parsing, quality linting with glossary enforcement, and DeepL API integration for machine translation
• Developed web application for material compliance management (EU REACH) using Django and modern web technologies
• Built automated infrastructure platform using Proxmox, Terraform, and Ansible — VM provisioning, configuration management, internal DNS, and fleet-wide security hardening across multiple subnets

DOKKI

AI-powered digital health product development

• Shaped the product vision, requirements, priorities, and technical target architecture of an AI-powered solution for processing medical documents.
• Designed LLM evaluation, data processing, data protection/governance aspects, and subsequent product development; technical prototyping with Codex.

• Development of a software-based DMS framework for the integration of QMS with a focus on compliance with ISO 17025 and MEASNET + TR6
• Participated in external DAkkS audits
• Created FMEA, quality deviation reports, Ishikawa diagrams, and 5 Whys reports for energy yield uncertainties and WTGs
• Highlighted critical variables (e.g., wind speed, wind direction, terrain) influencing uncertainty and evaluated data quality to implement QA/QC measures
• Identified main sources of uncertainty in energy yield assessment (e.g., model assumptions, data variability) and assessed impact on energy yield projections
• Systematically calibrated and validated the model using reference data (e.g., LiDAR) to reduce uncertainty while implementing continuous quality improvement processes (PDCA, RCA, SPC, FMEA)

• Patent application: European patent drafting and prosecution
• LLM practicing: Developed LLM-based tools for automated patent data retrieval, applying Python scripting to accelerate technical reviews.