• Define what AI the enterprise will and will not use, on what basis, and under what conditions. • Define what decisions the enterprise is willing to delegate to AI, where humans must remain in the loop, where humans review after the fact, and where AI can act autonomously. • Establish the intake process for AI use cases, including AI impact assessments and risk classification. • Establish who is accountable when AI acts, including model and use case owners in the business, independent risk and compliance review, and audit assurance. • Define how the enterprise governs third-party and vendor AI, including procurement criteria, vendor assessment standards, contractual terms, and ongoing review. • Serve as the primary governance advisor to VP- and Director-level stakeholders; translate strategic intent into actionable governance commitments. • Lead the engagement team and own both the engagement roadmap and engagement deliverables from development to adoption.

• Desenvolver e evoluir soluções utilizando Inteligência Artificial Generativa e Large Language Models (LLMs). • Projetar e implementar agentes inteligentes e fluxos baseados em arquiteturas multiagentes. • Desenvolver APIs e serviços backend para integração de soluções de IA com sistemas corporativos. • Contribuir para definições arquiteturais, boas práticas de engenharia e evolução da plataforma de IA. • Atuar na implementação de soluções baseadas em RAG (Retrieval-Augmented Generation), Prompt Engineering e avaliação de modelos. • Promover qualidade, observabilidade, segurança e escalabilidade das soluções desenvolvidas. • Colaborar com times multidisciplinares e stakeholders na definição e evolução de soluções alinhadas às necessidades do negócio. • Apoiar tecnicamente o time na resolução de desafios complexos e na adoção de novas tecnologias relacionadas à IA.

• Lead cross-functional engineering and architecture teams in the end-to-end delivery of complex AI systems, ensuring technical excellence and delivery quality. • Define and enforce reference architectures, technical patterns, delivery standards, and accelerators for enterprise AI adoption (leveraging Azure capabilities where appropriate). • Provide senior-level architectural oversight on programs, guiding the design of advanced, distributed, AI-powered applications across multi-system enterprise environments. • Drive the integration of modern AI capabilities—such as generative AI, intelligent/agentic systems, and Model Context Protocol (MCP)—into production-ready software. • Champion best practices for performance, scalability, reliability, observability, and MLOps/LLMOps across all AI delivery engagements. • Serve as a trusted technical advisor to executive stakeholders (CIO, CTO, CDO), helping them define enterprise AI strategies and prioritize high-value opportunities. • Lead technical workshops, architecture reviews, and visioning sessions to align solution direction with business outcomes. • Represent 3Cloud’s technical leadership in client engagements, partner discussions, and industry events, effectively communicating complex AI and engineering concepts. • Oversee a portfolio of large-scale AI delivery programs, ensuring architectural integrity, risk mitigation, and alignment with client strategy. • Provide proactive technical guidance to remove delivery blockers, solve architectural challenges, and ensure teams are building scalable and secure solutions. • Mentor senior architects, tech leads, and engineers, raising the overall technical bar of the practice.

• Own the design and evolution of core capabilities within our internal AI agent platform to support high-impact business workflows and AI agents. • Define and improve deployment patterns, shared services, and orchestration approaches that enable complex agent use cases to run reliably. • Partner with business stakeholders to translate high-priority strategic goals into scalable AI agent solutions. • Work hands-on with builders to debug issues, answer technical questions, and unblock day-to-day development while also improving the underlying platform. • Investigate failing repositories, run code locally when needed, and fix infrastructure or environment issues at the root cause. • Build and maintain shared services—such as reusable modules, integration layers, logging patterns, and data access approaches—that multiple teams can leverage. • Drive decisions around observability, governance, permissions, reliability, and cost-aware usage to ensure our AI transformation is sustainable and secure. • Help set technical direction for how new capabilities should be designed so we create durable foundations rather than one-off solutions. • Collaborate across engineering and business functions to identify and shape new high-value opportunities for agent-led innovation.