Artificial Intelligence Engineer

Há 2 dias

Ituiutaba, Brasil 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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