Principal GenAI Engineer

Nityo Infotech Corporation; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Nityo Infotech Corporation in NYC invites a Principal GenAI Engineer to lead design and deployment of LLM-based systems, focusing on robust, scalable GenAI solutions.

You will own codebase in Python, LangGraph, LangChain, and SQL; mentor engineers; collaborate with product and data teams; drive cloud deployments on AWS/Azure/GCP with CI/CD.

Qualifications

• 8–13 years in ML/AI systems.
• 2+ years hands-on with LLMs (RAG, agents, prompt engineering).
• Proficient in Python, LangGraph, and SQL.
• Experience deploying GenAI on AWS/Azure/GCP.

Responsibilities

• Develop and optimize LLM-based solutions.
• Codebase ownership in Python and SQL.
• Cloud deployment and CI/CD integration for GenAI apps.
• Cross-functional collaboration with product owners, data scientists, and SMEs.
• Mentoring and guidance to the engineering team on ML/LLM practices.

Skills

ML/AI systems experience

LLM experience

Python

LangGraph

SQL

RAG

Prompt engineering

LangChain

Cloud deployment

Tools

LangChain

LangGraph

Job description

Role :: Principal GenAI Engineer

Location: NYC, NY - 3 Days WFO

Experience Level: Principal (10 years)

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
• Experience deploying GenAI systems on AWS / Azure / Google Cloud Platform

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, Google Cloud Platform, 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.