Senior AI Engineer (GraphRAG & Automation - Sofia/Ruse)
Ontotext, doing business as Graphwise, is looking for a Senior Software Engineer focused on AI integration and data pipelines to help build the next generation of GraphRAG and Graph Automation products.
You will work at the intersection of knowledge graphs, LLM systems, and data orchestration, building scalable pipelines that power retrieval-augmented generation (RAG), semantic enrichment, and automated knowledge workflows.
If you enjoy building AI-driven backend systems, data pipelines, and production-grade LLM integrations, this role is for you.
As Senior AI Engineer you will:
- Design and implement GraphRAG pipelines that combine knowledge graphs with LLM-based retrieval systems
- Build scalable data ingestion, transformation, and enrichment pipelines for structured and unstructured data
- Integrate LLMs and embedding models into production systems (RAG, semantic search, agent workflows)
- Work with workflow automation tools (e.g., n8n) to orchestrate complex data and AI pipelines
- Build clean, production-grade APIs for AI and data services
- Optimize performance of retrieval, indexing, and transformation pipelines
- Ensure reliability, observability, and scalability of AI-driven backend systems
- Collaborate with product, data, and backend teams to design end-to-end AI features
- Collaborate with customer-facing teams and with Research on the research agenda
- Debug complex issues across distributed data and AI pipelines
- To filter applicants who do not read job descriptions, please submit, in your application form a valid JSON-LD string in the Address field (check https://json-ld.org/playground/) with your name and a link to your LinkedIn profile
Your Profile:
- 4+ years of experience in backend or data engineering (Java, Python, or similar strongly typed language)
- Experience designing and building LLM pipelines, ETL workflows, backend data processing systems, and prompt engineering, including evaluationStrong understanding of APIs and backend system design
- A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches
- Hands-on experience with structured and/or unstructured data processing
- Experience integrating external services or APIs in production systems
- Strong problem-solving skills and ability to work with complex data flows
- Degree in Computer Science, Engineering, or equivalent practical experience
- Proficiency in English, both written and verbal
Nice to have:
- Experience with RAG systems, LLM orchestration, or AI retrieval pipelines
- Familiarity with vector databases, embeddings, or semantic search systems
- Experience with n8n or similar workflow automation tools
- Knowledge of knowledge graphs, RDF, SPARQL, or GraphDB-like systems
- Experience with Elasticsearch / OpenSearch or other search indexing systems
- Exposure to LLM frameworks (LangChain, LlamaIndex, etc.)
- Experience with multi-agent systems or complex agentic workflows
- Experience with distributed systems and scalable data infrastructure
- Familiarity with cloud platforms (AWS/GCP/Azure) and containerized deployments
- Experience in early-stage development – you enjoy the zero-to-one phase
- Actively contributed to relevant open-source projects or publications
Skills Required
- 4+ years of experience in backend or data engineering (Java, Python, or similar strongly typed language)
- Experience designing and building LLM pipelines, ETL workflows, backend data processing systems, and prompt engineering including evaluation
- Strong understanding of APIs and backend system design
- Deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches
- Hands-on experience with structured and/or unstructured data processing
- Experience integrating external services or APIs in production systems
- Strong problem-solving skills and ability to work with complex data flows
- Degree in Computer Science, Engineering, or equivalent practical experience
- Proficiency in English, both written and verbal
- Submit a valid JSON-LD string in the Address field of the application with your name and LinkedIn link
- Experience with RAG systems, LLM orchestration, or AI retrieval pipelines
- Familiarity with vector databases, embeddings, or semantic search systems
- Experience with n8n or similar workflow automation tools
- Knowledge of knowledge graphs, RDF, SPARQL, or GraphDB-like systems
- Experience with Elasticsearch / OpenSearch or other search indexing systems
- Exposure to LLM frameworks (LangChain, LlamaIndex, etc.)
- Experience with multi-agent systems or complex agentic workflows
- Experience with distributed systems and scalable data infrastructure
- Familiarity with cloud platforms (AWS/GCP/Azure) and containerized deployments
- Experience in early-stage development (zero-to-one)
- Actively contributed to relevant open-source projects or publications
What We Do
Graphwise enables organizations to unlock ROI for enterprise AI by delivering the most comprehensive and trusted industry solution in the field of knowledge graphs and semantic AI technologies. As enterprises pour millions into AI investment, Graphwise delivers the critical knowledge graph infrastructure to ensure enterprises are ready to realize the technology’s full potential, is trusted, and can be implemented at scale. Graphwise, which is the result of the merger between tech visionaries Ontotext and Semantic Web Company, has over 200 employees worldwide, with offices located across North America, Europe and APAC.
