Graph AI Engineer: Knowledge Graphs & LLM-Driven Apps

Veriipro; Chicago (IL); Presencial; USD 120.000 - 160.000; Tempo integral

Resumo da vaga

Veriipro is seeking an AI Engineer to design and implement AI/ML solutions leveraging graph databases and knowledge graphs. The role focuses on building intelligent applications, graph representations of data, and scalable pipelines.

You will work with Neo4j, Cypher, and cloud platforms, applying Generative AI, LLMs, and RAG to enterprise use cases. Collaboration with data scientists and business teams is essential for success.

Qualificações

• Hands-on experience building AI solutions with graph databases and knowledge graphs.
• Experience with Neo4j, Cypher or similar graph query languages.
• Design and implement AI/ML solutions in Python.
• Develop graph-based data models for intelligent apps.
• Work with LLMs, Generative AI, RAG, and Agentic AI.
• Build APIs and data pipelines for AI-driven apps.

Responsabilidades

• Design, develop, and implement AI/ML solutions using Python.
• Build and maintain Knowledge Graphs and Graph Databases.
• Develop graph-based AI solutions to improve data discovery.
• Work with LLMs, Generative AI, RAG, and Agentic AI.
• Integrate graph technologies with AI models and enterprise apps.
• Work with graph databases such as Neo4j or Amazon Neptune.
• Develop APIs and data pipelines to support AI-driven apps.
• Collaborate with data scientists, architects, and business teams.

Conhecimentos

Python

AI/ML

Graph databases

Knowledge graphs

Neo4j/Cypher

GenAI/LLMs

REST APIs

Cloud platforms

Graph Neural Networks

LangChain

LlamaIndex

Vector databases

Semantic search

Agentic AI

Multi-Agent Systems

Descrição da vaga

Veriipro is seeking an AI Engineer to design and implement AI/ML solutions leveraging graph databases and knowledge graphs. The role focuses on building intelligent applications, graph representations of data, and scalable pipelines.

You will work with Neo4j, Cypher, and cloud platforms, applying Generative AI, LLMs, and RAG to enterprise use cases. Collaboration with data scientists and business teams is essential for success.