AI Trainers in Germany

Train large language models (LLMs) in valuation, financial modelling and investment analysis.

• 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.

• 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.

People and organizations face particular challenges in today's world: Technological changes have a massive impact on the way we live and work, while the fast pace and amount of change around us require ever faster adaptability and ability to absorb information, resilience and confidence in our own identity. While change projects in companies were characterized by a clear start and end date until a few years ago, we are now faced with permanent change. I support people and organizations in accepting and living with this permanent change.

• 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.

• Annotated datasets for machine-learning applications, ensuring accuracy, consistency, and full adherence to detailed guidelines
• Created custom evaluation rubrics for AI training data, improving labeling quality and standardization
• Categorized and evaluated data according to project-specific rules while working directly with AI systems
• Conducted quality reviews and provided feedback to refine AI model performance

• 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).

• Claude Code subagents, MCP, Pydantic V2, pytest, bandit

• Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.

• Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.

• 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.

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

• 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.

• 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.

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