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Artificial intelligence engineer

Nova Lima
Insight Global
Anunciada dia 17 setembro
Descrição

Job Title: AI Engineer (Search Engine)About the Company: Insight Global's ClientType: 10 month extending contractCompensation: Shared upon initial WhatsApp screening call Location: Fully RemoteWorking 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 overtimeMust-Haves 1+ years of experience as a Search Engineer or AI EngineerSearch Technology Experience – OpenSearch, building scalable search systems2+ 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 teamNice to Have SkillsFamiliarity with AWS infrastructure (IAM, VPC, S3, etc.)Exposure to RAG architectures, specifically textual RAG use casesProject 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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