Job Description

Our client, an international AI development company based in New York, is currently seeking a LLM Scientist- Agentic Frameworks to lead strategic product development efforts in a fast-paced and collaborative environment. 

This role will focus on advancing agent-based  architectures powered by multimodal Retrieval-Augmented Generation (RAG) capabilities. The ideal candidate will  have expertise in agentic frameworks and multimodal information processing using tools like CLIP, BLIP, and similar  models. You will contribute to building intelligent agents that can perceive and interact with multiple types of input,  such as text, image, and structured content. 

Key Responsibilities

Agentic Frameworks & Architecture:

Design agentic architectures capable of autonomous task execution and coordination 

Develop intelligent pipelines that support long-term memory, reasoning, and dynamic action planning

Multimodal RAG Systems:

Research and implement multimodal RAG pipelines that combine text and visual inputs

Integrate tools such as CLIP, BLIP, or equivalent models into context retrieval and generation workflows 

Work on unifying structured and unstructured modalities for enhanced knowledge access

Research & Collaboration:

Work closely with engineers and scientists to build production-ready prototypes

Contribute to internal research efforts, knowledge-sharing sessions, and long-term roadmap planning 

Qualifications & Skills

Strong understanding of agentic AI frameworks (e.g., LangGraph, AutoGPT) and autonomous systems

Proven experience with multimodal models like CLIP, BLIP, or equivalent

Practical expertise in Retrieval-Augmented Generation (RAG) architectures

Proficient in Python and experienced with machine learning frameworks, preferably PyTorch

Very strong English communication skills, both written and verbal (essential for global collaboration)

Comfortable communicating with both technical and non-technical stakeholders

Research-oriented mindset with the ability to convert theory into real-world systems

Self-driven, curious, and collaborative personality, comfortable in iterative and agile R&D environments