LLM Engineer

Há 4 dias

Cabo de Santo Agostinho, 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 Responsibilities RAG & VectorStore Systems: Build and maintain end-to-end RAG pipelines for context-augmented generation Integrate and optimize vector databases (FAISS, Pinecone, Weaviate, Milvus) Support the engineering backbone of agentic and RAG-based AI systems Protocol Engineering: Implement Model Context Protocol (MCP) to maintain stateful LLM interactions Build Agent-to-Agent (A2A) communication layers for multi-agent orchestration Enable persistent memory and context sharing across model calls Platform Enablement: Collaborate with cross-functional teams to productionize models and workflows Ensure seamless data flow and model integration across services Qualifications & Skills Deep experience with vector databases and RAG architecture Familiarity with MCP (Model Context Protocol) and A2A (Agent-to-Agent) design patterns Solid background in Python, cloud-based ML pipelines, and containerization tools Experience in operationalizing LLM-based systems in production Detail-oriented with a strong engineering mindset Effective communicator with technical and non-technical stakeholders Self-driven and adaptable in a fast-paced R&D environment Very strong English communication skills, both written and verbal (essential for global collaboration) Experience with Contract Analysis and automated document understanding Nice to Have: Experience with invoice parsing / invoice understanding, including extracting structured data from financial documents Experience in NLP-to-SQL, natural language querying, or generating structured database queries from unstructured text Familiarity with legal-tech, document intelligence, or enterprise knowledge extraction systems Experience with observability, vector hygiene, and evaluation frameworks for RAG/agentic systems Hands-on work with high-throughput, low-latency AI pipelines

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