Especialista em IA e Orquestração de Agentes
J
JobgetherInformation Technology
Based in BrazilContractMiddle
Salary not disclosed
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Job Details
- Required Skills
- DockerNode.jsPythonSQLLangChain
Requirements
- Proven experience developing applied AI solutions, particularly AI agents, LLM-based applications, and intelligent workflow orchestration.
- Strong proficiency in Python or Node.js for backend development and AI pipeline implementation.
- Hands-on experience with agent and orchestration frameworks such as LangChain, LangGraph, CrewAI, and n8n.
- Solid knowledge of RAG architectures, including chunking, embeddings, re-ranking, and context-window optimization.
- Experience working with vector databases or search technologies such as Pinecone, Weaviate, Chroma, or Azure AI Search.
- Familiarity with AI providers and models including OpenAI, Azure OpenAI, and open-source models such as LLaMA and Mistral through Ollama.
- Advanced prompt engineering skills and practical experience with tool calling and function calling.
- Experience integrating systems through REST APIs and webhooks, as well as working with structured databases such as SQL.
- Experience deploying and operating AI solutions in Microsoft Azure, including cloud monitoring, observability, and scalability.
- Knowledge of Docker and CI/CD practices, particularly with GitHub Actions.
- Strong software architecture knowledge and the ability to work independently across the complete development lifecycle.
Responsibilities
- Develop applied AI solutions involving intelligent agents, LLM-based systems, and workflow orchestration.
- Build backend services and AI pipelines covering ingestion, vectorization, querying, retrieval, and response generation.
- Design and implement agent and workflow architectures using technologies such as LangChain, LangGraph, CrewAI, and n8n.
- Build and optimize RAG architectures, including chunking, embeddings, re-ranking, and context-window optimization.
- Integrate AI models and services through prompt engineering, tool calling, function calling, and specialized APIs.
- Connect AI solutions with external systems through REST APIs, webhooks, and structured databases such as SQL.
- Deploy, monitor, scale, and maintain AI solutions in Microsoft Azure environments.
- Apply software engineering best practices across architecture, development, testing, deployment, observability, and continuous improvement.
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