AI Engineer with an industrial automation background — I build production-oriented AI systems with agent workflows, RAG, human-in-the-loop controls, and reliable software engineering around them.

Germany · LinkedIn · sjcode.de

Deterministic regression gates for LLM agent behaviour, across providers, in CI. Scores generation and retrieval separately, stores each run, and fails the build when results regress against the last known-good baseline. No LLM judge in the gate — every scorer returns the same score for the same output, so a moved number always means the agent moved.

Python · evaluation · multi-provider · regression testing · CI · RAG · standard-library-only

AI-powered email processing for vacation-rental operations. Classifies incoming mail, extracts booking data, drafts replies, and keeps a mandatory human approval step before sending. Includes multi-tenancy, WhatsApp notifications, observability, CI, and Railway deployment.

Python · Flask · LangGraph · MongoDB Atlas · React · TypeScript · Langfuse · Railway

Full-stack AI system for industrial maintenance teams. Combines tasks, fault catalogs, machine knowledge, shift workflows, and source-backed RAG with role-aware access control, local fallbacks, CI quality gates, and Docker deployment.

Python · Flask · SQLAlchemy · PostgreSQL/pgvector · OpenAI-compatible providers · React · Docker

Provider-neutral regression gates for LLM agents. Replays fixed cases, scores generation and retrieval separately, stores comparable runs, and fails CI when quality, coverage, or error rate regresses.

Python · Multi-provider evaluation · RAG metrics · Deterministic scoring · GitHub Actions

Privacy-conscious learning platform for technical apprenticeships with a structured curriculum, exam-style questions, progress tracking, content-generation workflows, and review gates for AI-generated learning material.

FastAPI · Python · Learning analytics · Privacy-by-design · Content workflows

My software and AI automation website, built as a performant Next.js application with SEO-focused structured data and a production deployment workflow.

Next.js · React · TypeScript · Netlify

Agent Systems: durable workflows, tool use, human approval, provider routing, auditability

AI Engineering: RAG, retrieval pipelines, structured outputs, evaluation, observability, guardrails

Backend: Python, FastAPI, Flask, SQLAlchemy, REST APIs, background jobs

Data: PostgreSQL, pgvector, MongoDB, Redis, SQLite

Frontend: React, TypeScript, Next.js, Tailwind CSS

Production: Docker, GitHub Actions, Railway, CI/CD, typing, testing, secret-safe configuration

I am currently concentrating on reusable AI-engineering infrastructure rather than isolated demo applications:

• agent runtime patterns for resumable, approval-gated workflows
• secure MCP/tool integration patterns
• production RAG with measurable retrieval quality
• context engineering with AGENTS.md, architecture decisions, and structured project knowledge
• privacy-safe automation that separates private operational context from publishable reference implementations

• Human-in-the-loop for consequential AI actions
• Tests, linting, typing, and build checks as release gates
• Explicit architecture and decision records instead of hidden conventions
• No secrets or private operational data in public repositories
• Demo and local-fallback modes where practical so projects can be evaluated without production credentials