Pessoa Engenheira de IA Sênior
New
Based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonFastAPIMicroservicesLLMGenerative AILangChain
Requirements
- Strong hands-on experience building production solutions with Generative AI and LLM-based systems.
- Deep understanding of LLMs, embeddings, RAG architectures, vector databases, and semantic search.
- Advanced Python skills and experience building APIs, preferably with FastAPI or similar frameworks.
- Experience with AI orchestration frameworks such as LangChain, LlamaIndex, CrewAI, Semantic Kernel, or LiteLLM.
- Solid software engineering background including microservices, APIs, relational databases, and system integration.
- Ability to evaluate model outputs, diagnose issues, and implement technical improvements.
- Strong awareness of AI safety, privacy, security, and responsible development practices.
- Experience with autonomous agents, multi-agent systems, or agentic workflows.
- Familiarity with tool use, function calling, MCP, and external system integrations.
- Experience with vector databases such as pgvector, Pinecone, Weaviate, Qdrant, or FAISS.
Responsibilities
- Design, develop, and maintain applications powered by LLMs, RAG pipelines, autonomous agents, copilots, and intelligent automation systems.
- Build orchestration flows connecting models, APIs, tools, vector databases, and enterprise data sources.
- Develop and optimize RAG pipelines, including embeddings, chunking strategies, retrieval, reranking, and response validation.
- Implement guardrails, fallback mechanisms, evaluation frameworks, and hallucination reduction techniques.
- Develop APIs and backend services to expose AI capabilities for internal and external consumption.
- Apply prompt engineering, context engineering, and tool-calling strategies to improve model performance and reliability.
- Define and monitor metrics related to latency, cost, quality, safety, and system effectiveness.
- Investigate model failures and propose architectural or data-driven improvements.
- Collaborate with Data, ML, Platform, Product, and Engineering teams to deliver integrated AI solutions.
- Ensure security, privacy, traceability, and responsible AI usage across all implementations.
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