Principal GenAI Engineer – Knowledge Graph & Semantic Systems

Employment Type: Full Time

Experience Level: Principal

About Turing:

Based in Palo Alto, California, Turing is the world’s first AI-powered tech services company. It has reimagined tech services from the ground up with AI by offering AI-vetted and matched talent, AI-accelerated development, and access to AI transformation experts who have built many of the most iconic Silicon Valley companies.

Founded in 2018, the company has experienced tremendous growth with over two million global developers on its Talent Cloud and 900+ clients. Turing has received numerous awards, including Forbes’s “One of America’s Best Start-up Employers” and recognition from The Information and Fast Company as one of the most innovative companies globally.

About the Role:

Turing is hiring a Principal GenAI Engineer with strong expertise in LLMs and Knowledge Graphs to lead enterprise-scale AI implementations for Fortune 500 clients. This role focuses on building Graph-powered RAG systems (Graph-RAG) that combine structured semantic reasoning with advanced LLM architectures to deliver scalable, explainable, production-grade AI solutions.

What We’re Looking For:

8-13 years of experience in ML/AI systems
2+ years hands-on experience with LLMs (RAG, agents, prompt engineering)
5+ years of production experience working with Knowledge Graphs
Strong proficiency in Python, LangChain, LangGraph, and SQL
Experience deploying GenAI systems on AWS / Azure / GCP

Mandatory Knowledge Graph Expertise:

Design and scale enterprise Knowledge Graph architectures
Develop ontologies, taxonomies, and semantic data models
Implement entity resolution, relationship extraction, and graph enrichment
Experience with Neo4j, Amazon Neptune, or similar graph databases
Strong hands-on experience with Cypher (or similar graph query languages)
Build hybrid retrieval systems combining Knowledge Graphs + vector databases
Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability