Generative AI Engineer

Micasa Global; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Micasa Global is seeking a Principal GenAI Engineer to lead enterprise-grade AI initiatives for Fortune 500 clients in New York. You will architect and deploy RAG-driven solutions, combining semantic reasoning with advanced LLMs to deliver scalable, explainable AI products.

You will own Python-based codebases (LangChain/LangGraph) and guide cross-functional teams through design, deployment, and CI/CD workflows, while mentoring colleagues on ML best practices.

Qualifications

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

Responsibilities

• Lead enterprise-scale GenAI implementations for Fortune 500 clients.
• Develop and optimize LLM-based solutions with RAG and agent architectures.
• Own codebase in Python; ensure reusable, scalable components.
• Drive cloud deployments with CI/CD across AWS/Azure/GCP.
• Mentor engineering team and promote ML best practices.

Skills

ML/AI systems

LLMs (RAG, agents, prompt engineering)

Python

LangGraph

SQL

Cloud deployment (AWS/Azure/GCP)

Tools

LangChain

LangGraph

Job description

Experience Level: Principal (8-14 years)

About the Role

Client 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.

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 / GCP
• 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.