• Design and build knowledge bases, ontologies, and taxonomies that structure organizational information for AI consumption
• Implement RAG (Retrieval-Augmented Generation) pipelines that connect LLMs to company knowledge bases for accurate, grounded responses
• Develop chunking strategies, embedding pipelines, and retrieval optimization for vector search systems
• Create and maintain knowledge graphs that capture relationships between entities, concepts, and processes
• Build data ingestion pipelines that process documents, wikis, databases, and unstructured content into searchable knowledge stores
• Evaluate and improve retrieval quality through relevance testing, re-ranking, and hybrid search tuning
• Collaborate with domain experts to capture, validate, and maintain institutional knowledge

• 3+ years in data engineering, knowledge management, or information architecture roles
• Experience building RAG systems with vector databases (Weaviate, Pinecone, pgvector, ChromaDB)
• Strong Python skills and experience with embedding models and NLP pipelines
• Knowledge of ontology design, taxonomy development, and knowledge graph technologies (Neo4j, RDF, SPARQL)
• Understanding of document processing, text extraction, and content classification techniques
• Experience with search systems (Elasticsearch, Solr) and information retrieval fundamentals
• Strong communication skills for working with domain experts to capture and structure knowledge

Frequently Asked Questions:

What does a Knowledge Engineer do?

A Knowledge Engineer designs and maintains knowledge bases, ontologies, and graph structures that power search, recommendations, and AI systems. They build taxonomies, write SPARQL or Cypher queries, and bridge subject-matter experts and software engineers.

How much does it cost to hire a Knowledge Engineer?

U.S. salaries for this role have climbed sharply in recent years. Knowledge Engineers in the U.S. command an average U.S. salary of $12,000 per month compared with an average Latin American salary of $5,000 per month, creating potential savings of up to 58%. The LatAm talent pool is deep across countries like Argentina, Brazil, Colombia, and Mexico, and most candidates South places have direct experience supporting U.S. companies.

Can a U.S.-based company hire a remote Knowledge Engineer from Latin America?

Yes, and most U.S. teams already do. South contracts the Knowledge Engineer locally, handles compliance and payroll, and bills you monthly. The hire joins your tools, standups, and Slack like a domestic teammate. Time-zone alignment is the unlock: same-day collaboration without the async lag of EMEA or APAC.

What skills should I look for when hiring a Knowledge Engineer?

Real graph or knowledge-base experience (Neo4j, Stardog, RDF/OWL), comfort writing ontologies, Python or Java for tooling, and the ability to translate domain expertise into formal structure. LLM and RAG context is a strong plus today.

What tools and software should a Knowledge Engineer know?

The standard stack varies by team, but expect fluency with: Protege, Neo4j, Stardog, RDF, OWL, SPARQL, Python, LangChain, Pinecone, Confluence. Beyond tools, look for judgment about when to reach for which tool, and the discipline to document choices for the next person on the project.

How is a Knowledge Engineer different from a AI Engineer?

An AI Engineer typically focuses on model training and inference. A Knowledge Engineer focuses on the structured data and ontologies that feed those models. For RAG-heavy products, both roles often coexist.

How can South help me hire a Knowledge Engineer?

South sources LatAm knowledge engineers with graph database and ontology design experience. You see a shortlist in seven to ten days and onboard inside three weeks. Schedule a free call here to get started!

Interview Questions:

• Walk me through an ontology you have designed end-to-end.
• How do you decide between RDF and a property graph for a project?
• Describe how you have integrated a knowledge graph with an LLM.
• Walk me through how you maintain a taxonomy as the business evolves.
• How do you collaborate with subject-matter experts who are not technical?
• Describe a SPARQL or Cypher query that solved a hard problem.
• How do you measure quality and coverage of a knowledge base?
• Walk me through your tooling around ontology versioning.
• How do you handle conflicting facts or sources?
• When would you not use a knowledge graph for a project?

Related Roles:

• AI Engineer
• Data Engineer
• Machine Learning Engineer
• AI Researcher
• AI Developer
• Back-End Developer

Related Skills:

• SPARQL
• Python
• LangChain
• Pinecone