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# GenAI Architect

- **Company:** AIT Global, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Business Analytics Applications, Data Analysis, Cloud Computing, Databases, Data Architecture, Information Engineering, Data Infrastructure, Python (Programming Language), Software Deployment, SQL Databases, Unstructured Data, Enterprise Data Management, Data Processing, Large Language Models, Multi-Agent Systems, Generative AI, Data Analytics, Enterprise Integration, Virtual Agents, GPT, Data Pipelines, Microservices
- **Published:** August 26, 2026
- **Apply:** https://www.dice.com/job-detail/947b49ba-4f42-43a7-b6ed-88759ca98183

## About the Role

* 15+ years of software/data engineering experience
 * Strong Python
 * Generative AI / LLM / RAG
 * Agentic AI / AI Agents / Agent Orchestration
 * Strong Data Engineering & Analytics
 * SQL and modern data platforms
 * Experience with APIs, microservices, and cloud platforms
 * Experience with LLM frameworks such as LangChain, LangGraph, or similar
 * Strong understanding of AI architecture, data architecture, and enterprise integration
 * Excellent communication and stakeholder management skills

## Description

We are looking for a Senior GenAI Architect with strong expertise in Data Engineering, Analytics, Generative AI, and Agentic AI workflows. The candidate will design and build scalable AI-driven solutions that leverage enterprise data, LLMs, RAG, and agent-based architectures., * Design and develop GenAI and Agentic AI applications using Python and modern AI frameworks.

 * Build scalable data pipelines, data processing, and analytics solutions.
 * Develop LLM/RAG-based applications integrating enterprise data sources.
 * Design and implement agentic workflows, AI agents, orchestration, and tool integrations.
 * Work with structured and unstructured data to support AI and analytics use cases.
 * Integrate LLMs with APIs, databases, knowledge sources, and enterprise platforms.
 * Implement evaluation, monitoring, security, and governance for GenAI solutions.
 * Collaborate with Data Engineers, Data Scientists, Architects, and business stakeholders.
 * Drive solutions from POC/prototype through production deployment.


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