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

Job Description

## 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 or Tallinn – 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 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 background in Java, Python, or TypeScript (4+ years) - 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.>