Artificial Intelligence Engineer

4 semanas atrás

Brazil Insight Global Tempo inteiro

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.

******Structure: CLT ONLY (This cannot change to PJ) *****

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 providd 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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