Machine Learning Engineer (LLMs Knowledge Graphs)

TL;DR

Machine Learning Engineer (LLMs Knowledge Graphs): Developing and deploying AI products by building specialized knowledge graph architectures integrated with LLMs with an accent on RAG and semantic search. Focus on optimizing Cypher/SPARQL queries, designing complex entity relationships, and building production-grade graph systems.

Location: Fully remote within Latin America

Company

AI talent partner that helps U.S. companies build and scale world-class AI, ML, and Data teams using top talent from Latin America.

What you will do

• Design and implement knowledge graph architectures using property graph (Neo4j) or RDF-based models.
• Transform structured and semi-structured data into optimized graph structures and query them using Cypher or SPARQL.
• Integrate knowledge graphs with LLMs using Retrieval-Augmented Generation (RAG) architectures.
• Build robust APIs using FastAPI and application services for relationship strength analysis and network traversal logic.

Requirements

• 5+ years of experience developing and deploying machine learning models in production.
• Proficiency in Neo4j, RDF frameworks, Cypher, and SPARQL.
• Experience with AWS Neptune, GraphDB, or Memgraph for production-grade Knowledge Graphs.
• Expert-level Python development skills for enterprise-grade applications.
• Strong knowledge of embeddings, vector databases, and semantic search techniques.
• Excellent verbal and written communication skills in English.

Culture & Benefits

• Ownership through equity participation.
• Annual company retreat and company-wide winter break.
• Education bonus for continuous learning.
• Paid time off and tailored career roadmaps.
• High-performance culture with a focus on transparency and growth.