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Há 4 dias

Poços de Caldas, Brasil Talentra Tempo inteiro

Our client, an international AI development company based in New York, is currently seeking a "LLM Engineer" to lead strategic product development efforts in a fast-paced and collaborative environment.This role will focus on implementing scalable vector store integrations, building retrieval pipelines, and enabling advanced communication protocols between intelligent agents.Key ResponsibilitiesRAG & VectorStore Systems:Build and maintain end-to-end RAG pipelines for context-augmented generationIntegrate and optimize vector databases (FAISS, Pinecone, Weaviate, Milvus)Support the engineering backbone of agentic and RAG-based AI systemsProtocol Engineering:Implement Model Context Protocol (MCP) to maintain stateful LLM interactionsBuild Agent-to-Agent (A2A) communication layers for multi-agent orchestrationEnable persistent memory and context sharing across model callsPlatform Enablement:Collaborate with cross-functional teams to productionize models and workflowsEnsure seamless data flow and model integration across servicesQualifications & SkillsDeep experience with vector databases and RAG architectureFamiliarity with MCP (Model Context Protocol) and A2A (Agent-to-Agent) design patternsSolid background in Python, cloud-based ML pipelines, and containerization toolsExperience in operationalizing LLM-based systems in productionDetail-oriented with a strong engineering mindsetEffective communicator with technical and non-technical stakeholdersSelf-driven and adaptable in a fast-paced R&D environmentVery strong English communication skills, both written and verbal (essential for global collaboration)Experience with Contract Analysis and automated document understandingNice to Have:Experience with invoice parsing / invoice understanding, including extracting structured data from financial documentsExperience in NLP-to-SQL, natural language querying, or generating structured database queries from unstructured textFamiliarity with legal-tech, document intelligence, or enterprise knowledge extraction systemsExperience with observability, vector hygiene, and evaluation frameworks for RAG/agentic systemsHands-on work with high-throughput, low-latency AI pipelines

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