Artificial Intelligence Engineer

buscojobs Brasil; Santa Catarina; Teletrabalho; BRL 80.000 - 120.000; Tempo integral

Resumo da oferta

A technology services firm is seeking an AI Engineer (Search Engine) for a fully remote, 10-month contract. This role focuses on building and optimizing RAG models for search across large-scale medical documents. Candidates must have 1+ years of experience in search technology and 2+ years of Python development. Strong communication skills are essential, along with the ability to work independently. The position offers a flexible working schedule from 10AM to 6PM BRT.

Qualificações

• 1+ years of experience as a Search Engineer or AI Engineer.
• 2+ years of Python development experience.
• Ability to work full-time hours on a contract basis.

Responsabilidades

• Build and optimize RAG models for search across large-scale documents.
• Work on semantic chunking strategies and fine-tune LLMs.
• Collaborate with DevOps for production rollout.

Conhecimentos

Search Technology Experience – OpenSearch

Python development experience

Strong communication skills

Familiarity with LangChain

Descrição da oferta de emprego

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

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.

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
• 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

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.