As a leading expert in artificial intelligence, we are seeking an experienced engineer to join our team.
The primary focus will be building and optimizing Retrieval-Augmented Generation (RAG) models for search across large-scale medical and scientific documents. This involves pre-processing and embedding over half a billion documents to ensure they are searchable and contextually accurate.
You'll work on semantic chunking strategies, improving the automated evaluation pipeline, and fine-tuning LLMs for textual RAG use cases.
This is a hands-on, individual contributor role that requires experimentation, model development, and backend engineering with deployments to non-prod environments and collaboration with DevOps for production rollout.
A strong understanding of communication and collaboration is essential for success in this position.
To excel 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