• Liderar o time de desenvolvimento de IA • Orquestrar a comunicação entre stakeholders e time de executores • Gerenciar backlog de produto e priorização estratégica • Promover um ambiente de alta performance • Atuar na intervenção de impedimentos e fomentar feedback contínuo • Colaborar com diferentes agentes autônomos

• Design and build RAG and automation workflows inside our VPC environment. • Create integrations across engineering, communication, and knowledge-management systems (Jira, Confluence, Slack, Google Drive, Git). • Develop AI-powered pipelines for code quality checks, ticket auto-classification, documentation updates, release notes, and meeting summaries. • Work on prompt engineering, model selection and routing (Haiku / Sonnet / Opus), and workflow optimization. • Build evaluation and regression frameworks for AI workflows to prevent model-upgrade regressions. • Create lightweight dashboards for engineering leads to surface delivery patterns and bottlenecks. • Own initiatives end-to-end: scope → design → ship → measure → iterate.

• Design, develop, and deploy AI/ML models for production environments. • Build and optimize AI-powered applications using large language models (LLMs) and generative AI tools. • Develop intelligent automation workflows, chatbots, recommendation systems, and predictive analytics solutions. • Fine-tune, evaluate, and improve machine learning models for performance and scalability. • Collaborate with software engineers and product teams to integrate AI features into existing platforms. • Work with structured and unstructured datasets for training and inference pipelines. • Implement prompt engineering strategies and retrieval-augmented generation (RAG) systems. • Monitor AI systems, troubleshoot issues, and continuously improve model accuracy and efficiency. • Stay updated with the latest advancements in AI, machine learning, and automation technologies. • Maintain documentation for AI models, systems, and workflows.

• Design, develop, and deploy AI and machine learning models for real-world applications. • Build and optimize AI-powered tools using technologies such as OpenAI, Claude, Gemini, or open-source LLMs. • Develop scalable APIs and backend services for AI applications. • Fine-tune machine learning models and improve model performance. • Work with structured and unstructured datasets for training and inference. • Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines. • Integrate AI solutions into web platforms, CRMs, and business systems. • Monitor AI model performance, reliability, and scalability in production. • Collaborate with cross-functional teams to define AI product requirements and technical roadmaps. • Stay updated on emerging AI technologies, frameworks, and best practices.