We're seeking a seasoned LLM Engineer to spearhead backend feature development for our Retrieval-Augmented Generation (RAG) service.
Your primary focus will be crafting and optimizing RAG models for search across vast medical and scientific datasets, including pre-processing and embedding millions of documents for accurate search results.
You'll work on semantic chunking strategies, enhancing the automated evaluation pipeline, and fine-tuning LLMs for textual RAG use cases. This role involves hands-on experimentation, model development, and backend engineering, with deployments to non-prod environments and collaboration with DevOps for production rollout.
* 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