Description

You will design and implement Neo4j-based knowledge graph architectures supporting biological and biomedical reasoning workflows.

Responsibilities

• Develop graph data models representing genes, proteins, pathways, diseases, and experimental conditions.
• Build and optimize Cypher queries for complex, multi-hop relationship exploration.
• Integrate Neo4j with AI/ML pipelines, including embeddings and GraphRAG workflows.
• Support ingestion of structured and semi-structured data from diverse sources.
• Implement performance tuning and scaling strategies for large, evolving graphs.

Required Skills

• 3+ years of hands-on experience developing with Neo4j in production.
• Strong expertise in graph data modeling and relationship-centric database design.
• Proficiency in Cypher query language, including optimization and profiling.
• Experience integrating Neo4j with backend services using Python or Java.
• Familiarity with knowledge graphs in scientific or biomedical domains.
• Understanding of data ingestion pipelines and ETL/ELT workflows.
• Ability to work independently in a research-driven environment.
• Residency in Latin America.
• Advanced English skills.

Preferred Skills

• Exposure to AI/ML systems, vector databases, or GraphRAG architectures.
• Experience deploying Neo4j in cloud environments (AWS, GCP, or Azure).

Key Skills

Education

Any Graduate