Engenheiro de Inteligência Artificial (Agentes Conversacionais e A2A)
New
J
JobgetherArtificial Intelligence
Brazil, 9:00 AM to 7:00 PM, Monday to Thursday, and 9:00 AM to 6:00 PM on FridaysFull-Time
Salary not disclosed
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Job Details
- Required Skills
- PythonFastAPIGrafanaReactLangChain
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.
Responsibilities
- 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.
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