Principal GenAI Engineer

Turing; New York (NY); Hybrid; USD 210,000 - 320,000; Full time

Job summary

Turing is seeking a Principal GenAI Engineer in NYC to lead enterprise-scale AI implementations for Fortune 500 clients. You will architect RAG-based systems and advanced LLM solutions that are scalable and explainable.

You will own Python codebases (LangChain/LangGraph) and SQL workflows, mentor engineers, and collaborate with product owners and data scientists to deploy GenAI apps on AWS, Azure or GCP with solid CI/CD.

Qualifications

• 8–13 years of experience in ML/AI systems.
• 2+ years hands-on with LLMs (RAG, agents, prompts).
• Strong Python, LangGraph, and SQL proficiency.
• Experience deploying GenAI on AWS, Azure or GCP.

Responsibilities

• Develop and optimize LLM-based solutions using RAG and agent architectures.
• Own Python codebases with LangChain/LangGraph; ensure reusable, scalable components.
• Assist cloud deployment of GenAI apps on Azure, GCP, or AWS with CI/CD.

Skills

LLMs (RAG, agents)

Python

LangGraph

SQL

Cloud deployment

Tools

LangChain

Job description

About the Role

• Location: NYC, NY – 3 Days WFO
• Employment Type: Full Time
• 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.

Required Qualifications

• 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.
• Experience deploying GenAI systems on AWS / Azure / GCP.

Key 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.