Engenheiro(a) de Inteligência Artificial (Agentes Conversacionais e A2A)

Accountabilities:

• • Design, develop, and continuously improve contextual, responsive, and human-centered conversational agents, including chatbots, copilots, and virtual assistants.
  • Architect multi-agent and Agent-to-Agent (A2A) solutions in which autonomous agents communicate, delegate tasks, and collaborate toward shared objectives.
  • Develop advanced prompt-engineering workflows and RAG pipelines to enable accurate use of proprietary knowledge bases while minimizing hallucinations.
  • Integrate Google AI technologies and foundation models, particularly Gemini and Vertex AI, into production-ready applications.
  • Implement tool-calling capabilities that allow agents to consume APIs, query databases, and execute actions across external and internal systems.
  • Establish and monitor quality metrics covering conversational clarity, context retention, accuracy, and overall agent performance.
  • Monitor inference health, application performance, scalability, and availability in production environments.
  • Build and maintain robust APIs and backend services supporting AI applications and agent architectures.
  • Implement observability, logging, and metrics solutions to identify issues and continuously improve reliability.
  • Contribute to the evolution of AI engineering practices, architectures, and development standards.

  Requirements:

  • Completed higher education, preferably with a specialization in technology, artificial intelligence, software engineering, or a related field.
  • Exceptional proficiency in Python, including object-oriented and asynchronous development.
  • Extensive experience building and maintaining robust APIs using FastAPI, Flask, or Django.
  • Advanced practical experience with LLM orchestration frameworks, particularly LangChain, LangGraph, and Langflow.
  • Experience working with the Google AI ecosystem, especially Gemini and Vertex AI APIs and services.
  • Strong understanding of agentic AI concepts, including autonomous reasoning patterns such as ReAct, Plan-and-Solve, and A2A workflows.
  • Experience implementing and optimizing vector databases such as Qdrant, Pinecone, Milvus, Weaviate, or pgvector.
  • Practical experience with production observability, logs, and metrics using tools such as Grafana.
  • Experience with containerized environments and orchestration platforms such as Rancher.
  • Strong analytical and problem-solving abilities, with a focus on building reliable, scalable AI solutions.
  • Ability to work with autonomy, continuously learn emerging AI technologies, and translate experimentation into production-ready solutions.

  Nice to have:

  • Experience with LLM and RAG evaluation frameworks such as Ragas, TruLens, or LangSmith.
  • Knowledge of Model Context Protocol (MCP) and standardized connections between agents and data sources.
  • Experience implementing persistent long-term memory and context-management strategies for extended conversational sessions.
  • Knowledge of AI application performance optimization, including latency reduction and token consumption management.

  Benefits:

  • Remote work model.
  • Full-time employment.
  • Working hours from 9:00 AM to 7:00 PM, Monday to Thursday, and 9:00 AM to 6:00 PM on Fridays.
  • Opportunity to work on advanced applications of conversational AI, autonomous agents, RAG, and multi-agent architectures.
  • Exposure to modern AI technologies and the Google AI ecosystem.
  • Opportunity to contribute to innovative technology initiatives with meaningful real-world impact.
  • Professional environment focused on continuous learning, technical development, and innovation.