AI Trainers in Germany

• Built and validated SupportPiloten, an AI-powered content operations service; won the first paying pilot customer
• Tested agentic workflows with Claude Code and Codex for analysis, research, documentation, and prototyping
• Continued developing my own AI product; training in AI governance, AI compliance, and the EU AI Act (ongoing)

Driving AI model quality at scale — evaluating prompt-response accuracy, flagging edge cases, and maintaining SLA-compliant workflows across distributed global teams.

• Analyse AI prompts and side-by-side model outputs to assess response quality, factual accuracy, relevance, consistency, and compliance with project evaluation guidelines.
• Perform fact-checking, data validation, troubleshooting, issue identification, and edge-case review to improve quality standards across AI training support workflows.
• Use Google Sheets, Google Docs, and browser-based tools to document findings, maintain evaluation logs, track issue patterns, and support workflow optimisation in a remote environment.
• Create clear written justifications, review summaries, and KPI-oriented reporting focused on accuracy, turnaround time, documentation completeness, defect identification rate, and SLA adherence.

Technical writer at a special-purpose machine manufacturer, implementing the requirements of the EU Machinery Regulation in the technical documentation and moderating FMEAs. Role: Technical Writer and FMEA Moderator

• Review and creation of German-language training data for AI models, with a focus on language quality, tone of voice, and suitability for target groups.
• Design of prompts and evaluation frameworks for quality assurance of AI responses.
• Prompt design and creation of AI training content in German and English.
• Language and voice training for AI models in German.

• Work as a German/English Language Expert evaluating and rating AI model responses for accuracy, reasoning quality, and natural language use at native/C-level proficiency in both languages.
• Perform structured data annotation and transcription tasks, applying detailed guideline-based scoring and edge-case judgment.
• Conduct Visual Quality Evaluation, assessing AI-generated and model-processed images and video for visual artifacts, factual/compositional accuracy, and adherence to detailed guideline criteria.
• Evaluate and annotate Text-to-Speech (TTS) model output, assessing pronunciation accuracy, prosody, naturalness, and audio quality against structured guideline criteria.
• Evaluate Speech-to-Speech (STS) model interactions, rating conversational audio for naturalness, tone, latency, and response appropriateness in real-time voice-to-voice exchanges.
• Manage concurrent workloads across several platforms simultaneously, prioritizing by task quality and throughput to meet weekly output targets.

• Use of AI tools (ChatGPT Pro, Google Gemini Plus, Claude Pro, Make.com Pro, n8n, Sora, Google Veo3, Octoparse Professional)
• Creation of high-quality sales pipelines in CRM Pipedrive
• Automated lead generation and qualification via web scraping and AI analysis
• Development of social selling and sales materials
• 56% lead-to-deal conversion; approx. €140k in own closings

• Producing German website, email and social media copy
• Localising technical website copy from English to German (IT, Tech, consumer products)
• Evaluating AI-generated German texts for factual accuracy, tone, and cultural relevance
• Auditing and correcting synthetic German datasets, reducing grammatical and stylistic errors

• 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.

• Synthesised 15+ SME case studies on AI-adoption barriers into a structured strategic analysis, and co-organised three startup events within Europe's largest regional high-tech network (1,400+ member companies).

Conventional Paradigm Test (CPT) – AI Evaluation & LLM Research

Development of an experimental evaluation approach to examine “paradigmatic closure” in Large Language Models — that is, the question of how far LLMs can recognize the basic assumptions, values, and limits of the paradigms within which they generate answers.

Design and testing of an additional approach to classic AI benchmarks that does not primarily measure factual correctness or task performance, but instead examines a model’s ability to recognize alternative perspectives, make implicit assumptions visible, and reflect on the limits of its own answer or interpretation framework.

Focus areas: development of evaluation criteria and test questions · LLM evaluation and comparative model analysis · prompt and response analysis · qualitative classification of model answers · study of epistemic compression and value leakage · benchmark and literature research · development of structured assessment and analysis methods

As part of CPT, existing AI evaluation approaches and benchmarks were analyzed, and a minimalist test protocol was developed that classifies model answers by response patterns such as DIRECT, CLARIFY, PLURALIST, REFUSE, and META-AWARE. TruthfulQA was used as the basis for experimental application and comparison with existing reference answers.

Technologies & Methods: Large Language Models (LLMs) · Generative AI · Prompt Engineering · AI Evaluation · TruthfulQA · Benchmark Analysis · Human-in-the-Loop Evaluation · Qualitative Content Analysis · Research & Literature Review

Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application

• Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
• Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users

• Codebase Evaluation & Problem Design: Designed and stress-tested complex software engineering problems against large open-source Python codebases (including pandas), requiring deep context acquisition and architectural understanding to produce well-scoped, realistic problem statements aligned to strict correctness guidelines.
• Agent Failure Analysis: Assessed LLM coding agent solutions for correctness and completeness, identifying meaningful failures across edge case handling, dtype behaviour, and multi-column NaN propagation logic; documented findings with precision for downstream evaluation use.
• Programmatic Test Suite Development: Authored comprehensive pytest suites to programmatically verify agent-generated solutions against defined requirements, with deliberate coverage of boundary conditions and failure modes not caught by naive implementations.
• Containerised Environment Engineering: Built and debugged Docker environments for reproducible agent execution, including git-based repository provisioning, dependency pinning with npm ci, and multi-stage Dockerfile authoring across Linux-based containers.

• Oversee AI-medical content pipeline operations, ensuring quality standards across multilingual publications (DE/EN/ES)
• Lead cross-functional collaboration with technical, medical, and creative teams to optimize content generation workflows
• Direct publication selection, review, and platform deployment processes with translation quality assurance

• RLHF & Model Evaluation: Evaluated, ranked, and refined Large Language Model (LLM) responses based on accuracy, reasoning quality, safety guidelines, and factual correctness.
• Data Annotation & Prompt Engineering: Created high-complexity prompts, edge-case scenarios, and gold-standard reference answers to train and fine-tune generative AI models.
• Quality Assurance & Verification: Conducted rigorous fact-checking, logical consistency validation, and multi-turn response optimization.