Software Engineer - RAG, Knowledge Graphs & Agentic Systems (m/f/d) - Anwendungsentwicklung, Ingenieur

We are looking for a Software Engineer – RAG, Knowledge Graphs & Agentic Systems (m/f/d)

(unlimited, full-time) Join our team at our location in Berlin, Baden-Baden, Verl, Tallinn, Oslo or Stockholm – flexible working conditions available

(Y)our Mission:

You will work embedded in our product and domain teams, building the AI-driven features that directly reach users. Your focus lies on RAG pipelines, knowledge graphs, context engineering, and multi-step agentic workflows. You translate AI capabilities into practical, reliable, and scalable product functionality. You will play a key role in developing Riverty’s AI platform to accelerate software engineering productivity. You will design and implement AI-driven solutions based on Large Language Models, agentic architectures, and knowledge graphs – building the foundation for automation and intelligent developer workflows across our tech organization.

Your key responsibilities:

• Build and optimize RAG pipelines, including retrievers, embeddings, indexing workflows, and evaluation logic.
• Integrate and leverage knowledge graphs to provide structured context for AI systems and agents.
• Implement agentic multi-step workflows using MCP clients, orchestration logic, and supporting tooling.
• Develop prompting strategies, chunking logic, and context preparation aligned with real product requirements.
• Integrate AI models into existing Riverty platforms.
• Conduct performance tuning, benchmarking, and cost optimization for RAG and agentic patterns.
• Work closely with Platform Engineers to adopt shared SDKs, gateway patterns, and architectural standards.
• Maintain clear documentation and contribute to a shared understanding of best practices in context engineering.

Your profile:

• Strong software engineering fundamentals in Java, Python, or TypeScript.
• Experience or strong motivation to work with RAG pipelines, retrieval systems, vector stores, or graph technologies.
• Ability to translate AI capabilities into real, user-facing product features.
• Structured, reliable working style with strong ownership and focus on delivery.
• High affinity for data-driven systems, search logic, and context architectures.
• Collaborative mindset and clear communication in cross-functional environments.
• Fluent in written and spoken English.