Principal AI Engineer – Agentic AI & RAG Systems (Enterprise LLM Solutions)

omiz staffing solutions (oss) • United State

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AI Summary

Lead enterprise-scale AI implementations for Fortune 500 clients by designing and deploying production-grade RAG systems and agentic architectures. Focus on scalable, explainable LLM solutions with Python, LangGraph, and cloud platforms (AWS/Azure/GCP). Mentor teams and collaborate cross-functionally to deliver impactful AI products.

Key Highlights

Principal-level role (8–14 years of experience, 10+ preferred) in AI/ML systems

Specialization in LLMs, RAG, and agentic architectures for enterprise solutions

Hybrid work arrangement (3 days onsite in New York City)

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Key Responsibilities

Design and deploy large language models with RAG and agent-based architectures for enterprise-scale AI solutions.

Build and maintain high-quality Python (LangChain/LangGraph) and SQL codebases with a focus on scalability and performance.

Collaborate with product owners, data scientists, and business SMEs to define AI project requirements and deliver impactful solutions.

Optimize GenAI deployments on cloud platforms (AWS/Azure/GCP) while ensuring robust CI/CD processes.

Provide technical leadership and mentorship to the engineering team on ML and LLM best practices.

Technical Skills Required

Python Large Language Models (LLMs) Cloud Platforms (AWS/Azure/GCP)

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Benefits & Perks

H-1B transfer candidates considered

Job Description

Job Title: Principal AI Engineer – Agentic AI / LLM

Location: New York City, NY

Work Arrangement: Hybrid – 3 days onsite per week

Employment Type: Full-Time

Work Hours: EST overlap required

Note:

Required Experience: Principal Level 8–14 years of software engineering experience; 10+ years preferred

Visa Sponsorship: H-1B transfer candidates can be considered

About the Role

We are hiring a Principal AI 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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