What You Will Do

• Contribute to the technical direction and productization of a composable, multimodal GraphRAG and Graph Automation architecture that supports unstructured, semi-structured, and structured business data (including code and text).
• Translate advances in LLMs, vLLMs, multimodal learning, and knowledge graphs into a clear technical roadmap, aligning R&D researchers and software engineers on hypotheses, prototyping, acceptance criteria, and architectural decisions.
• Design, optimize, and implement scalable data ingestion, transformation, and enrichment pipelines, as well as caching and state management.
• Integrate various language models – such as encoder-only models, decoder-only generative LLMs, and embedding models – into production through clean, reliable APIs, and build complex automated workflows using tools like n8n that connect platform products such as GraphDB, Graph Modeling, and Semantic Analytics.
• Uphold the team’s high technical bar through reviews, AI evaluation standards, and hands-on contributions, debugging complex issues across distributed AI and data pipelines.
• Diagnose and remediate LLM hallucinations, gaps, and accuracy issues.
• Architect robust prompt-engineering strategies that balance quality, latency, and cost.
• Maximize LLM efficiency through token optimization and intelligent caching.
• Design for high concurrency, fault tolerance, and comprehensive observability.
• Thrive in ambiguous, rapidly evolving technical environments.
• Balance immediate tactical execution with long-term strategic product vision.

Our Stack

• Languages: Python, Java, Node.js
• Graph and semantics: the Graphwise platform, including GraphDB (our RDF graph database), and related products such as Graph Modeling and Semantic Analytics
• LLMs and RAG: LangChain/LlamaIndex, embedding models, and GraphRAG architectures
• Search and retrieval: Elasticsearch/OpenSearch, Lucene, and various other vector store connectors
• Streaming, storage, and ingestion: S3, Kafka for data, and PostgreSQL
• Automation and delivery: n8n for workflow automation (central to this role), Jenkins for CI/CD, GitLab for version control, Kubernetes and Docker for deployment, Grafana and Prometheus for observability

What makes a good candidate

• 4+ years of experience in backend, data engineering, or software engineering, with strong proficiency in Python and/or Node.js; experience with statically typed languages such as Java is a plus but not required.
• Extensive experience with workflow automation tools such as n8n, Haystack, LangDock, Airflow, or similar, and a solid understanding of API design and the integration of external services into complex data flows.
• Demonstrated experience integrating and deploying LLM-based systems (Azure AI, AWS Bedrock, Google Vertex, HuggingFace, etc.) to production, including prompt engineering, evaluation (RAGAS, LLM-as-a-judge), and building LLM and ETL pipelines for data ingestion and indexing.
• At least 1 year of experience with advanced RAG systems (beyond vanilla RAG) in production (not tutorials or demos).
• Full professional proficiency in English (our primary working language).

Note: Hands-on knowledge graph experience is not required. If you know how to ship LLM-integrated features and data/software engineering, we can get you up to speed on graphs.

Nice-to-have-s

• Hands-on experience with graph technologies (e.g., RDF(S), GraphDB, Apache Jena, Blazegraph, or similar) and graph embeddings, or professional experience integrating knowledge graphs with LLMs.
• MLOps exposure, including CI/CD for ML, experiment tracking, model versioning, and production monitoring.
• Familiarity with multi-agent workflows and related orchestration patterns.
• Familiarity with even-driven systems (e.g., Kafka/Redis)
• Experience with distributed systems and cloud platforms (AWS, GCP, Azure).
• Hands-on experience with schema languages (RDFS/OWL, SHACL, JSON Schema, etc.)
• Contributions to relevant open-source projects.

What you will get

• The opportunity to work on cutting-edge projects in AI, knowledge graphs, and enterprise data.
• Flexible working arrangements and a healthy work-life balance.
• Competitive compensation and performance-based bonus opportunities.
• Additional health insurance and well-being benefits.
• A collaborative, international environment with colleagues across Europe, North America, and APAC.
• Access to learning resources, knowledge-sharing initiatives, and opportunities for continuous professional growth.
• The chance to work alongside some of the leading experts in semantic technologies and AI.
• Team gatherings, company events, and opportunities to connect beyond day-to-day work.