Hi — I'm Laura, a software engineer building AI-native applications and platforms.

I specialize in designing and implementing production-grade AI systems — from agent orchestration and retrieval pipelines to full-stack interfaces and deployment workflows.

• 🧠 AI systems engineering — LLM orchestration, RAG pipelines, decision-support platforms
• 🏗 Full-stack architecture — TypeScript, Next.js, Node.js, monorepos, testing & CI/CD
• ☁️ Production mindset — reliability, observability, evaluation, and scalable system design
• 🎓 Bachelor's Degree in Business Informatics
• 💼 8+ years building complex applications in collaborative environments
• 🌍 Based in Germany — open to international opportunities

Currently focused on building real-world AI applications that operate reliably outside the lab.

• Agent orchestration & tool-calling workflows
• Retrieval-Augmented Generation (RAG)
• Structured evaluation & testing of LLM systems
• Prompt engineering & guardrails
• Decision-support system design
• AI system observability & quality monitoring
• Human-in-the-loop interfaces

	Mosel Flood Risk Intelligence Platform A production-style AI decision-support platform combining live sensor ingestion, rule-based risk scoring, retrieval-augmented evidence, and LLM-generated explanations — delivered through a real-time Next.js dashboard.
	Financial Document Intelligence Agent A RAG-powered Q&A system for financial documents — upload a 10-K or earnings report, extract key metrics as structured JSON (revenue, EPS, net income, YoY deltas), then ask natural language questions and get grounded answers with inline citations.

Flood Intelligence — architecture highlights

• Real-time environmental data ingestion
• Rule-based risk modeling
• Retrieval-Augmented Generation for explainability
• LLM-driven decision interpretation
• End-to-end full-stack system (API → orchestration → UI)
• Monorepo architecture with automated CI and quality gates

Stack: TypeScript · Next.js · Node.js · pnpm monorepo · Claude AI (Anthropic) · Vitest · SonarCloud

Financial Document Intelligence — architecture highlights

• Section-aware PDF chunking (SEC 10-K Item detection)
• ChromaDB vector store with document-scoped similarity search
• Structured metric extraction with JSON schema prompting (revenue, EPS, gross margin, guidance)
• RAG pipeline with grounded answers and inline citations
• FastAPI backend + Next.js App Router frontend with server-side proxy
• Multi-stage Docker builds with ONNX embedding model baked in

Stack: Python · FastAPI · Next.js · ChromaDB · fastembed ONNX · Claude AI (Anthropic) · pytest · SonarCloud

• AI orchestration platforms for real-world decision support
• Evaluation frameworks for reliable LLM workflows
• End-to-end AI product architectures

AI platform architecture · distributed systems · developer tooling · system reliability · human-AI interaction

AI platform engineering · agent systems · production LLM infrastructure · intelligent decision-support software

I enjoy exploring new technologies, building real systems, and continuously improving how intelligent software operates in production environments.