Jobs via Dice

Principal GenAI Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Principal GenAI Engineer in NYC, NY, focusing on LLMs and Knowledge Graphs. Requires 10+ years in ML/AI, 5+ years with Knowledge Graphs, and strong Python and SQL skills. Full-time position, expected duration over 6 months.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
Unknown
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πŸ—“οΈ - Date
March 18, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
On-site
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πŸ“„ - Contract
Unknown
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
New York, NY
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🧠 - Skills detailed
#AI (Artificial Intelligence) #Graph Databases #Azure #GCP (Google Cloud Platform) #ML (Machine Learning) #Cloud #Neo4J #Langchain #Python #AWS (Amazon Web Services) #"ETL (Extract #Transform #Load)" #Knowledge Graph #Amazon Neptune #Scala #SQL (Structured Query Language) #Databases
Role description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Infojini, is seeking the following. Apply via Dice today! Role: Principal GenAI Engineer – Knowledge Graph & Semantic Systems Location: Onsite – NYC, NY Hire Type: Full Time (No Contract) About The Role 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 β€’ 10+ years of experience in ML/AI systems β€’ 2+ years hands-on experience with LLMs (RAG, agents, prompt engineering) β€’ 5+ years of production experience working with Knowledge Graphs β€’ Strong proficiency in Python, LangChain/LangGraph, and SQL β€’ Experience deploying GenAI systems on AWS / Azure / Google Cloud Platform Mandatory Knowledge Graph Expertise β€’ Design and scale enterprise Knowledge Graph architectures β€’ Develop ontologies, taxonomies, and semantic data models β€’ Implement entity resolution, relationship extraction, and graph enrichment β€’ Experience with Neo4j, Amazon Neptune, or similar graph databases β€’ Strong hands-on experience with Cypher (or similar graph query languages) β€’ Build hybrid retrieval systems combining Knowledge Graphs + vector databases Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability