We're seeking a skilled AI Model Optimizer to join our team.
The primary focus will be on building and optimizing Retrieval-Augmented Generation (RAG) models for search across large-scale medical and scientific documents.
* Building and optimizing RAG models for search
* Pre-processing and embedding over half a billion documents to ensure they are searchable and contextually accurate
* Semantic chunking strategies and improving the automated evaluation pipeline
* Fine-tuning Large Language Models (LLMs) for textual RAG use cases
To be successful in this role, you'll need:
* 1+ years of experience as a Search Engineer or AI Engineer
* Search Technology Experience - OpenSearch, building scalable search systems
* 2+ years of Python development experience, including API creation, model training, testing, and general backend programming
* Familiarity with LangChain for building LLM workflows using tools, memory, and retrieval
Prior experience with AWS infrastructure (IAM, VPC, S3, etc.) and RAG architectures, specifically textual RAG use cases is preferred.