Related skills
python rag langchain sparql neo4j📋 Description
- Design, build and maintain large-scale knowledge graphs and RDF stores.
- Architect autonomous AI agents for multi-step reasoning over graph data.
- Develop graph-aware RAG pipelines blending structured data with unstructured docs.
- Extend context with live data sources: web, APIs, and databases.
- Implement agent memory, reflection, and self-correction for reliability.
- Collaborate with platform engineers to deploy on cloud infra (AWS/GCP/Azure).
🎯 Requirements
- 5+ years of software engineering with strong Python (or Java/Kotlin).
- Hands-on prod experience with major graph databases (Neo4j, Neptune, TigerGraph).
- Knowledge of Cypher, SPARQL, Gremlin at production query complexity.
- Experience building LLM-powered agents or pipelines (LangChain, LangGraph, LlamaIndex, CrewAI, Semantic Kernel).
- Solid understanding of RAG architectures: chunking, vector stores, hybrid retrieval, re-ranking.
- Familiarity with prompt engineering, few-shot learning, LLM evaluation techniques.
🎁 Benefits
- Amazing work culture (collaborative & supportive; 5 days a week).
- Awesome colleagues from Meta, Google, LinkedIn and startup veterans.
- Competitive compensation.
- Flexible working hours.
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