Remote GenAI Engineer

Job Description

Job Description

A well-respected, stable financial services organization based in Los Angeles, CA is seeking an AI Engineer to design and implement next-generation Generative AI solutions that power real-world business applications. This role focuses on building RAG (Retrieval-Augmented Generation) pipelines , vector databases , and multi-modal AI systems that transform unstructured data into scalable, intelligent tools.

The ideal candidate will be a hands-on builder with experience developing AI pipelines and models in production environments - ideally coming out of Big Tech or top-tier AI teams - and comfortable leveraging modern AI frameworks and orchestration tools to deliver production-grade solutions.

What You'll Do

Build and deploy RAG pipelines , AI agents , and multi-modal applications using frameworks such as LangChain, LangGraph, or similar orchestration tools.

Design and maintain vector databases and data ingestion pipelines to move unstructured data into embedding-based storage for retrieval and modeling.

Develop, fine-tune, and integrate LLMs and Generative AI models into production workflows and business systems.

Collaborate cross-functionally with engineers, data scientists, and product teams to design and deliver end-to-end AI solutions.

Optimize AI model performance, scalability, and cost in cloud environments (AWS, Azure, or GCP).

Document system designs, workflows, and technical processes to ensure scalability and reproducibility.

Stay current with emerging GenAI technologies, frameworks, and best practices .

What We're Looking For

5-7 years of experience in software or AI engineering , with at least 3+ years focused on machine learning, LLMs, or applied AI .

Strong programming skills in Python with experience using frameworks such as PyTorch , TensorFlow , or similar.

Hands-on experience working with tools such as HuggingFace , LangChain / LangGraph , n8n , or comparable orchestration frameworks (specific tools not mandatory).

Proven experience building RAG systems and integrating LLMs with external or enterprise data sources.

Background in