Artificial Intelligence Engineer

2 semanas atrás

João Pessoa, Brasil buscojobs Brasil Tempo inteiro

Overview

Job Title: AI Engineer (Search Engine)

About the Company: Insight Global's Client

Type: 10 month extending contract

Compensation: Shared upon initial WhatsApp screening call

Location: Fully Remote

Working Hours: 10AM - 6PM BRT (9AM-5PM EST)

Interview Process: immediate interviews available - 2 rounds can close by end of next week.

Responsibilities
  • Build and optimize retrieval-augmented generation (RAG) models for search across large-scale medical and scientific documents.
  • Handle pre-processing and embedding for over half a billion documents to ensure searchability and contextual accuracy.
  • Develop semantic chunking strategies, improve the automated evaluation pipeline, and fine-tune LLMs for textual RAG use cases.
  • Conduct hands-on experimentation, model development, and backend engineering with deployments to non-prod environments.
  • Collaborate with DevOps for production rollout and maintain strong communication with the team.
Must-Haves
  • 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
  • Strong communication skills – able to work independently while providing clear updates to the team
Nice to Have Skills
  • Familiarity with AWS infrastructure (IAM, VPC, S3, etc.)
  • Exposure to RAG architectures, specifically textual RAG use cases
Project Overview

We are hiring one AI / LLM Engineer (Mid-Level) to join a small, focused team building backend features for an existing Retrieval-Augmented Generation (RAG) service. Your primary focus will be building and optimizing RAG models for search across large-scale medical and scientific documents, including 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. The role involves hands-on experimentation, model development, and backend engineering, with deployments to non-prod environments and collaboration with DevOps for production rollout. While this is a heads-down, individual contributor role, strong communication and collaboration with the team are essential.

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