Technical Writing Expert

• Responsible for day-to-day operations
• Responsible for schedules and quality
• Planned and managed staffing for 35 employees

Site 1: GERMANY | MAGDEBURG - C60

Large-scale industrial construction project in hydrogen sector, located in Magdeburg, Germany.

Coordinate and supervise civil, structural and infrastructure activities within a complex, safety-critical industrial environment.

• Manage day-to-day site coordination between contractors, suppliers, engineering teams and multidisciplinary construction interfaces.
• Monitor execution against drawings, technical specifications, QA/QC requirements, HSE standards and project schedules.
• Follow up construction progress, technical issues, non-conformances and corrective actions.
• Participate in site inspections, walkdowns and construction coordination meetings.
• Support commissioning readiness, SAT preparation, punch-list closure and final handover.

Site 2: GERMANY | KAISERSESCH – C53

SmartQuart – E.ON / Elogen | Hydrogen Electrolyser

• Provided on-site Site Management and Technical Coordination support during Elogen's intervention at the Kaisersesch electrolyser installation.
• Participated in site briefings and coordinated technical queries arising during execution.
• Followed up outstanding technical actions and ensured required information was communicated to relevant site stakeholders.
• Reported site status, technical constraints and outstanding actions to the Elogen project team.

Site 3: GERMANY | C68 - SLOVAKIA

Elogen / Veolia | Industrial Gas / Hydrogen Construction Project

• Coordinate infrastructure and structural activities within a complex industrial construction environment.
• Supervise and follow up civil / structural construction and installation works.
• Coordinate contractors, suppliers, engineering and construction teams.
• Monitor compliance with drawings, specifications, HSE and quality

Further Development of the Integrated Capacity Management Platform InKa

Rail Infrastructure

• Power BI shared datasets and reports via DirectQuery on Amazon Redshift (timetable, construction and capacity data)
• Automated report testing with Playwright in Docker containers, version control with GitLab
• Concept and implementation of user communication (information page, changelog, known bugs)

Initial Situation:

The integrated capacity management platform (InKa) is a monitoring and analysis platform for data from timetable and capacity management: customer orders, infrastructure, timetable operations, construction, as well as route capacity and width. The data is stored and linked in a structured data warehouse. This makes it possible to create complex KPIs across different source systems and display current and future route utilization as well as construction activity across the network. The goal is to improve strategic, medium-term and intra-year planning and provide users with easy access to capacity management analyses.

Measures:

• Enablement: Introduction of Power BI with training and workshops for development teams.
• Consulting & Design: Problem analysis, concepts and solution consulting for reporting requirements; implementation of reporting solution plans.
• Data Model: Development of shared datasets with DirectQuery on Amazon Redshift and import tables; DAX measures for utilization and capacity KPIs.
• Development: Data analysis, data transformation and visualization in Power BI; extension and migration of business applications to Power BI; AI-supported development in VS Code with GitHub Copilot.
• Test Automation: Automated testing of Power BI reports with Playwright; setup of the test environment in Docker containers.
• User Communication: Concept and implementation of a central information page with hot news, changelog and known bugs, access from reports with report filters, updates via Power Automate.
• Deployment: Management of Power BI Deployment Pipelines across the DEV, TEST and PROD environments.
• Agile Working: Work in an agile team according to SAFe (Scaled Agile Framework); refinement of user stories and features including effort estimates in Jira; version control with Git and GitLab.
• Quality & Documentation: Reviews for quality assurance, error analysis and resolution, technical documentation in AsciiDoc and Confluence.

Technologies: Power BI, DAX, Power Query, DirectQuery, Amazon Redshift, Git / GitLab, Deployment Pipelines, Playwright, Docker, AsciiDoc, Power Automate, Jira, Confluence, VS Code with GitHub Copilot, SAFe

Results:

• Capacity management KPIs from multiple source systems combined on one platform
• Transparency regarding current and future route utilization and construction activity across the network
• Easy access to analyses for users
• Automated regression testing of Power BI reports
• User communication implemented: central information page with hot news, changelog and known bugs, directly accessible from the reports

I have worked on the development of technical products from the concept phase through to production-ready design. My responsibilities included creating 3D CAD models and assemblies, designing components, developing plastic, sheet metal, milled and cast parts, as well as creating production drawings and technical documentation.

I worked closely with design, development, manufacturing and project teams to develop functional, cost-effective and producible solutions. In addition to designing new and existing products, my work also included optimizing existing assemblies in terms of cost, weight, ease of assembly and manufacturability.

CAD/Engineering Software:

CATIA Siemens NX SolidWorks Autodesk Inventor Autodesk Fusion Autodesk Revit Rhino 3D AutoCAD

Areas of focus:

Product development 3D CAD design Assembly development Technical product design Parametric modeling Detailed design Production drawings Design for Manufacturing (DfM) Change and variant management Prototype development Technical documentation

Through my combination of design understanding and technical engineering expertise, I can support products from the initial idea through design and development to production release.

• Evaluated AI outputs and source material for accuracy, relevance, clarity and usability across search-quality, annotation, language and multimodal tasks.
• Applied practical insight into how source quality, metadata and permissions affect reliable AI retrieval; no claim of chatbot engineering or platform implementation.

• Responsible for Confluence architecture and content strategy; aligned knowledge areas, navigation, page types, and templates with workflows.
• Developed responsibilities, review cycles, terminology, and editorial standards for maintainable knowledge systems.
• Built and continuously developed the information architecture, service offering, and content of the company website.
• Customer projects in the areas of Confluence knowledge structures and workflow management.

• Requirements Engineering
• Process optimization and introduction of collaborative working models
• Process modeling with BPMN
• Analysis, specification, and documentation of logistics processes, IT systems, and data models
• Development of conceptual solutions
• Use case creation and documentation
• Data analysis with SQL
• Administration of Jira & Confluence

• LLM Benchmarking: Evaluated and benchmarked 1,000+ LLM-generated responses for technical correctness, code efficiency, safety, and strict adherence to complex technical prompt constraints.
• Mathematical Auditing: Executed highly granular structural audits of model-generated code and multi-step mathematical derivations, documenting root-cause logic gaps, syntax errors, and hallucinations.
• Prompt Architecture: Authored and stress-tested high-complexity mathematical, logical, and coding prompts designed to uncover edge-case failures and assess advanced reasoning capabilities.
• Model Alignment: Led RLHF and SFT workflows, utilizing Google Collab and interactive Python scripts to parse massive datasets of model outputs and deliver highly detailed preference rankings.

• Low voltage (NSHV and UV)
• Medium voltage (medium-voltage systems)
• Creating circuit diagrams
• Creating technical documentation
• Technical support
• Supporting technical projects

• Stabilized the cross-site workplace/printer rollout and transferred it into reliable operational processes. Ongoing hospital operations, new naming conventions, new print servers in a different AD structure, Kerberos dependencies, management systems and inconsistent inventory data.
• Took over technical workstreams and escalated risks at an early stage. Interface between IT operations, project organization, internal employees and external service providers; independently responsible for critical technical workstreams without formal overall project management.
• Fully consolidated the central master/rollout data. Correlated serial number, MAC, IP, location and old/new device references from several sources and established them as a shared working basis for rollout, follow-up work, location and toner processes.
• Technically stabilized the printer rollout. 717 devices in the consolidated rollout scope, including 431 replacement devices; 114 documented deviations. Controlled, created or validated around 2,000 printer/queue objects.
• Proposed Location B as an independent work package and managed it technically. Consolidated 216 old devices into 88 new devices; implemented this in around 10 working days with 1-2 external technicians. Prepared ClearPass, TXT/mapping and ORBIS offset topics in parallel.
• Made several thousand existing TXT/VBS client assignments suitable for bulk migration. Defined old/new mapping, new print servers, Follow-Me, queue, suffix and tray logic; developed checking/correction logic for incorrect manual names.
• Built and productively verified cross-domain FollowMe. Used the historical username attribute to map between the old and new AD domains; stopped further expansion when data quality was no longer reliable.
• Developed the technical approach for hardware/queue reconciliation. Correlated standard lists, print server objects and real devices using IP/SNMP; defined rules for MAC, serial number, IP, location, toner, trays, keep/remove/rename, duplicates and rollback.
• Built AI-assisted automation under time pressure. Developed and stabilized PowerShell/XML tools with independently developed process, data and validation logic, test cases, logging, DryRun/guardrail approaches, error analysis and live validation.
• Designed an HTTP/web platform as a target architecture that was not introduced into production. Technically designed ASP.NET Core 8, .NET Worker, SQLite and PowerShell 5.1 for job control, target/actual comparison, logging/audit, recovery, queueing and locking.
• Operationalized toner, location and operational processes. Consolidated TA-Cockpit location data; established toner/order logic with prioritized run lists; supported Follow-Me, duplex and Scan2Mail pilots.
• Worked out the KRITIS/emergency topic in detail. Structured and visualized printer operations during power/infrastructure failures, coordinated internally and supported the integration into the emergency concept.
• Coordinated the workplace/IGEL client rollout with 1.200+ thin clients from a technical and operational perspective. Prepared the project from 11/2025 and continued it from mid-09/2026. Technically reviewed and structured the process from unpacking and labeling/inventory to setup; handed over operational unpacking/label coordination to a colleague so several employees could work in parallel.
• Personally prepared around 120 thin clients for production. Together with a technical colleague, primarily responsible for finding a solution for automated IGEL-OS-12 installation; productive automation is currently still open.
• Saved around 83 hours of manual ClearPass data entry. Created a PowerShell/import script for ClearPass and demonstrably saved the network colleague around 83 hours of manual entry for 1.200 thin clients; at the same time, significantly reduced the risk of manual errors.
• Supplied formal project management with technical results. Operational results, technical feasibility, process structure and status from the responsible workstreams serve as an important basis for steering by the official project manager.
• Documentation & handover. Documented rollout, AQrate, TA-Cockpit and automation processes, conducted training and tested, configured and prepared around 25 digital medical carts for operation.

• Data Engineering: Design and implementation of scalable data pipelines
• Legacy migrations to Microsoft Fabric (Lakehouse, Dataflows Gen2, Pipelines)
• BO Universe migrations to MS Fabric / Semantic Models / Power BI
• Taskforce for data-driven transformation projects involving Azure Fabric / Oracle / MSSQL

• Developed Python scripts to analyze CSV datasets.
• Automated report generation and data visualization.
• Improved workflow efficiency through data processing automation.

• 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.

• Provided technical leadership for a 4-engineer team delivering 3 major client platforms in 12 months with microservices architecture and scalability solutions — 100% of scoped majors shipped ahead of schedule vs. planned milestones (baseline: prior releases often slipped 1–2 sprints).
• Ran AI model evaluation and model outputs evaluation on LLM/AI vendor APIs: safety, completeness, instruction adherence, and groundedness review before go-live; cut escaped bad outputs in AI-integrated release checklists from recurring UAT findings to near-zero on final promote.
• Drove API development and performance optimization for payment, exchange, and AI services; fail-closed error handling and payload validation reduced integration rework cycles by ~35% vs. the first AI integration pass.
• Applied software testing, testing frameworks, code quality assurance, and code refactoring with continuous integration gates; first-pass PR acceptance improved across the team and production hotfixes on AI adapters dropped noticeably after review standards landed.
• Owned DevOps practices: Docker, GitHub Actions, Jenkins-compatible pipelines, and version control workflows — cut deployment time ~50% vs. pre-automation baseline and stabilized releases across 3 client environments.
• Implemented verifier/oracle-style pass-fail checks in container sandboxes (Harbor/Terminal-Bench aligned); wrote technical documentation so failures cleared in one review cycle.
• Led cross-functional collaboration with product and client stakeholders; translated AI evaluation scores and risk findings into plain-language briefs for non-technical partners, unblocking go/no-go decisions without extra engineering meetings.
• Used agile methodologies for sprint planning and backlog ownership; mentored engineers so mid-level contributors owned AI adapter modules independently by mid-engagement.