Design, structure, and evolve solutions based on Artificial Intelligence (LLMs, RAG, autonomous agents, copilots), defining functional requirements, product architecture, quality criteria and overseeing implementation through to production, with a focus on delivering measurable value to the business and users.
Drive strategic, impact-oriented discovery
Identify, structure, and prioritize AI application opportunities based on real problems, assessing technical feasibility, risks, return on investment (ROI), and alignment with the product's strategic positioning.
Evangelize and raise organizational AI maturity
Serve as an internal reference for AI-Native applications, providing training for Product Managers, designers and technical teams, disseminating best practices, limitations, responsible-use standards and contributing to the consolidation of an AI-driven culture.
Establish metrics, governance and quality standards for AI
Define and monitor AI-specific indicators for AI-based solutions (accuracy, acceptance rate, impact on conversion or productivity), ensuring compliance with ethics, privacy, information security principles and LGPD.
Enhance the Product process productivity with AI
Apply AI tools to optimize discovery, writing and refinement of PRDs, rapid prototyping (vibe coding), creating user-simulating agents for testing, data analysis and accelerating experimentation cycles, raising the efficiency and quality standards of Product deliveries.
Requirements
Experience in digital product management, with direct involvement in conceiving and delivering AI-Native applications into production, taking responsibility for product decisions and functional architecture.
Experience with LLMs (GPT, Claude, Gemini or similar), RAG, autonomous agents, embeddings, AI APIs, REST API integrations and use of AI-assisted development tools.
Ability to discuss architecture with engineering, evaluate technical trade-offs, structure clear requirements and lead discovery focused on metrics and business outcomes.
Experience designing experiences for probabilistic systems, including managing uncertainty, feedback mechanisms, bias mitigation, safety for generative systems and compliance with LGPD.
Ability to translate technical concepts into accessible language, run workshops and training sessions, influence stakeholders without formal authority and work collaboratively in multidisciplinary environments.