Artificial Intelligence Engineer

Há 2 dias

Itajaí, Brasil buscojobs Brasil Tempo inteiro

Overview

Job Title: AI Engineer (Search Engine)

About the Company

Insight Global's Client

Details
  • 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.
Priority Scheduling for candidates who
  • Submit resume properly
  • Are available for immediate interviews
  • Need to meet Recruiter over video prior to sending application to client
Requirements
  • Ability to work full-time hours on a contract basis (10AM - 6PM BRT)
  • Strong communication skills
  • CLT ONLY ( This cannot change to PJ)
  • No health benefits provided in CLT benefits
  • Needs to be able to start within 2-3 weeks
  • Paid on a monthly basis, no overtime
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 Engineers (1 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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