Principal AI Software Engineer (Agentic AI)
Remote, Europe (EU only). Full-time.
Overview
We are hiring a Principal AI Software Engineer to lead the design and delivery of production‑grade agentic AI systems for a fast‑growing, product‑led technology company headquartered in Germany. This role is critical in shaping AI architecture, guiding engineering teams, and building intelligent systems that operate reliably at scale.
Responsibilities
- Design, build, and operate production‑grade agentic AI systems and complex AI workflows.
- Architect scalable backend services using Node.js and Python.
- Implement AI solutions using LLMs, embeddings, prompt engineering, and RAG (Retrieval‑Augmented Generation).
- Design and integrate storage solutions including PostgreSQL, Redis, and vector databases.
- Lead integrations with complex APIs and third‑party services.
- Leverage and extend agent frameworks such as LangChain, LangGraph, or custom equivalents.
- Work with workflow and automation platforms (n8n, Appmixer, Make.com, or similar).
- Define technical standards, review designs, and mentor senior and mid‑level engineers.
- Drive architecture decisions in a product‑led, fast‑scaling environment.
Required Qualifications & Skills
- Proven experience delivering production‑ready agentic AI systems or complex AI orchestration workflows.
- Strong hands‑on expertise in JavaScript / Node.js and Python.
- Deep understanding of data storage technologies (PostgreSQL, Redis, vector databases).
- Experience with prompt engineering, embeddings, and RAG architectures.
- Solid understanding of API design and system integrations.
- Experience leading technical direction and mentoring engineers.
- Ability to operate independently in a senior, principal‑level role.
- Strong architectural thinking paired with hands‑on implementation skills.
Nice‑to‑Have Skills
- Experience with Pinecone, Weaviate, or similar vector databases.
- Familiarity with low‑code / workflow automation platforms.
- Background in scalable SaaS or AI‑first product companies.
- Exposure to cloud‑native architectures and distributed systems.
- Experience scaling AI systems under real commercial load.