Data Engineer for AI
At Graphwise, we help enterprises transform fragmented data into connected, intelligent systems using Knowledge Graphs, semantic technologies, and modern AI architectures.
About the role
We’re looking for a strong candidate with experience in data engineering and an interest in AI systems, data modeling, and large-scale information architectures. You don’t need to be a semantic technologies expert already - what matters most is solid engineering thinking, curiosity, and the ability to work with complex data problems.
Main Responsibilities:
- Design and build robust data pipelines for structured and unstructured data
- Integrate and harmonize data from multiple enterprise systems
- Work on AI-oriented retrieval and context architectures, including RAG and GraphRAG patterns
- Build workflows for extracting structured information from documents and text
- Contribute to scalable backend and data processing systems
- Collaborate with technical and business stakeholders to solve complex information challenges
- Explore and adopt modern AI, NLP, and data engineering technologies
Must-haves:
- Strong software engineering fundamentals
- Professional experience with Python, Java, or Scala
- Experience building backend systems or data pipelines
- Solid understanding of data modeling and ETL processes
- Familiarity with modern AI concepts such as: LLMs, RAG, vector databases, embeddings, or NLP workflows
- Experience with Git, CI/CD, and collaborative engineering practices
- Strong analytical and problem-solving skills
- Good communication skills in English
- Curiosity and willingness to learn new domains and technologies
Nice-to-haves:
- Knowledge Graphs or graph databases
- Semantic technologies such as RDF, OWL, SHACL, or SPARQL
- Ontology or taxonomy modeling
- NLP Basics: Basic understanding of Knowledge Extraction, specifically identifying and linking entities within text.
- NLP tooling such as SpaCy or similar libraries
- GraphRAG implementations
- Cloud platforms and distributed systems