A German-based AI software engineer with 25+ years of experience seeks permanent or part-time work.
They specialize in autonomous AI agents, LLM integration, and data platforms.
The developer has completed 40+ projects and uses 10+ programming languages, with a recent focus on AI-assisted development and autonomous workflows.
What happened
An AI software engineer based in Germany with 25+ years of professional development experience is seeking work on permanent or part-time roles (16–20 hours, or full-time temporarily) in remote or hybrid settings. The developer specializes in AI agents, LLM integration, data solutions, and app development, with a portfolio spanning autonomous workflow systems, AI-assisted back-office automation, SEO agents, and voice support assistants.
Why it matters
The profile demonstrates deep technical execution across both classical software engineering (10+ programming languages, 40+ completed projects) and modern AI systems—building autonomous agents that handle iterative improvement, multi-agent orchestration, knowledge base maintenance, and real-time voice interaction. This combination of proven delivery speed and AI-native architecture is relevant for companies building production AI systems.
What to watch
The developer is available for engagement in both remote and hybrid arrangements, with flexibility on commitment level (part-time or full-time). Key technical skills include Python, TypeScript, LangChain, prompt engineering, vector databases, and infrastructure (Docker, Cloudflare Workers). The portfolio includes both internal tools and customer-facing systems (invoice automation, support escalation, SEO auditing).
Ask the AI about this article →
This is a job-seeking profile from a software developer in Germany with a quarter-century of production experience. The portfolio reflects a deliberate pivot toward AI systems over recent years: earlier projects span iOS, Android, and enterprise back-end systems (C#, Java, C++), but the current focus is entirely on autonomous agents, LLM integration, and AI-assisted tooling. The project history shows execution depth rather than theoretical interest—each system listed includes architectural choices (e.g., YAML-based state for auditability, multi-tenant storage, crash-safe file-system primitives) and deployment context (customer premises, embedded monitoring, real-time audio over WebSocket). The developer explicitly mentions using AI-assisted development as a delivery method, which aligns with the technical focus: projects include systems for managing parallel AI coding agents, evaluating LLM outputs, and maintaining knowledge bases agenically. The range of tech stacks (from bare JavaScript with no dependencies to containerized Python services) suggests comfort moving between codebases and problem domains quickly—a claim reinforced by the claim of "25+ years of experience across languages, stacks and industries."
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