AI Technology Specialist

1 dia atrás


Brasília DF, Brasil beBeeEngineering Tempo inteiro

Job Title: AI Engineering Leader We are seeking a highly skilled and experienced AI engineering leader to spearhead our product development efforts in a fast-paced environment. This role focuses on implementing scalable vector store integrations, building retrieval pipelines, and enabling advanced communication protocols between intelligent agents. The successful candidate will be responsible for: RAG & VectorStore Systems Designing and maintaining end-to-end RAG pipelines for context-augmented generation Integrating and optimizing vector databases (FAISS, Pinecone, Weaviate, Milvus) Spearheading the engineering backbone of agentic and RAG-based AI systems Protocol Engineering Implementing Model Context Protocol (MCP) to maintain stateful LLM interactions Building Agent-to-Agent (A2A) communication layers for multi-agent orchestration Enabling persistent memory and context sharing across model calls Platform Enablement Collaborating with cross-functional teams to productionize models and workflows Ensuring seamless data flow and model integration across services The ideal candidate will possess: Required Skills and Qualifications Deep experience with vector databases and RAG architecture Familiarity with MCP and A2A 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 Experience with Contract Analysis and automated document understanding Bonus Points 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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