Lead enterprise-scale AI implementations for Fortune 500 clients by building scalable RAG systems and LLM-based solutions. Design and deploy production-grade AI architectures using Python, LangGraph, and cloud platforms. Requires 8-13 years of ML/AI experience with 2+ years in LLMs and strong technical leadership skills.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Title : Principal GenAI Engineer
Location : NYC, NY Hybrid/onsite
Job Type: Full time
H1b transfer also work
About the Role
Turing is hiring a Principal GenAI Engineer with strong expertise in LLMs to lead enterprise-scale AI implementations for Fortune 500 clients. This role focuses on building RAG systems that combine structured semantic reasoning with advanced LLM architectures to deliver scalable, explainable, production-grade AI solutions.
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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)
• Strong proficiency in Python, LangGraph, and SQL
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• Experience deploying GenAI systems on AWS / Azure / GCP
Roles & Responsibilities
• Develop and optimize LLM-based solutions: Lead the design and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Codebase ownership: Build and maintain/review high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
• Cloud integration: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
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