• Lead AI Agent Strategy: Architect and drive the technical evolution of our customer-facing AI agents, ensuring they deliver intuitive, intelligent, and highly reliable interactions. • Innovate with Super Agents: Spearhead the development of "Super Agents"—complex, multi-agent systems capable of reasoning, planning, and executing multi-step tasks across our platform. • Workflow Automation: Design and implement robust frameworks to automate complex business workflows that agents can reliably execute, reducing manual overhead and increasing operational efficiency. • Collaborate: Work closely with product managers, data scientists, and cross-functional engineering teams to translate business needs into scalable agentic solutions. • Technical Leadership: Provide hands-on technical guidance, set best practices for LLM integration, prompt engineering, and agent orchestration, while safeguarding scalability, security, and performance. • Mentorship: Mentor senior engineers on AI/ML best practices, agentic design patterns, and the integration of emerging AI technologies into production systems.

• Optimize the availability, performance and security of our cloud infrastructure. • Build internal libraries and DevOps guidelines to help Engineering squads own and maintain their infrastructure. • Craft and optimize our public APIs to provide a stellar developer experience to our technical customers. • Design and maintain robust data pipelines and platforms used by both internal teams and external customers. • Research and develop technical solutions and best practices that improve squads' development, release and maintenance experience. • Collaborate with all development squads.

Role Description Design and lead the evolution of enterprise-wide AI platforms and architecture to enable AI-powered products, intelligent workflows, and scalable digital transformation. This role combines enterprise architecture, platform engineering, and AI delivery to build solutions that are secure, scalable, and operationally viable. The AI Platform Architect ensures end-to-end alignment from strategy through implementation, balancing innovation, governance, and enterprise usability. - Define and drive enterprise architecture strategy across AI, data, cloud, and platform ecosystems - Architect and evolve enterprise AI platforms, including LLM pipelines, RAG systems, and agentic workflows - Design intelligent applications, copilots, and conversational AI solutions embedded into business processes - Lead end-to-end solution architecture, from concept and prototyping through implementation and scale - Develop target architectures, reusable patterns, and capability roadmaps for AI adoption - Design and manage hybrid, multi-cloud, and on-prem infrastructure (AWS, Azure, GCP, GPU environments) - Define and implement API strategy, integration patterns, and enterprise governance frameworks - Establish DevSecOps practices, including CI/CD, Infrastructure as Code, and automated deployment pipelines - Architect data platforms, knowledge systems, and analytics capabilities to enable AI and decision-making - Enable enterprise integration across ERP, CRM, data platforms, and workflow systems - Ensure security, compliance, and responsible AI practices aligned with regulatory requirements - Lead hands-on prototyping and solution validation to accelerate delivery and reduce risk - Provide senior stakeholder and board-level advisory on architecture decisions, risks, and trade-offs - Drive vendor evaluation, technology selection, and AI tooling strategy - Support and mentor engineering teams to ensure effective execution of architecture designs Qualifications - Strong experience in enterprise and solution architecture across complex environments - Ability to define architecture principles, governance models, and long-term technology strategy - Expertise in platform thinking and enterprise system design - Experience with AI/ML platforms, including LLMs, RAG, semantic search, and agent-based systems - Understanding of AI-enabled workflows, knowledge systems, and enterprise AI adoption - Ability to design scalable, production-grade AI platforms - Strong foundation in cloud (AWS, Azure, GCP), hybrid, and on-prem architectures - Experience with GPU infrastructure, model deployment, and inference optimization - Proficiency in APIs (REST, GraphQL), microservices, and distributed systems - Hands-on experience with DevSecOps, CI/CD pipelines, and Infrastructure as Code (Terraform) - Knowledge of data platforms, ETL, data warehousing, and analytics systems - Expertise in enterprise integration patterns and API governance - Strong understanding of security, identity (SSO, OAuth2/OIDC), and compliance frameworks (ISO27001, GDPR, etc.) - Ability to influence senior stakeholders and cross-functional teams - Strong communication translating technical architecture into business outcomes - Experience leading engineering and architecture teams - Balanced approach to innovation, governance, cost, and scalability Requirements - 12+ years of experience in software engineering, AI/ML systems, or platform architecture, with strong focus on Generative AI - Proven experience acting as Senior Principal / Enterprise Technical Architect within large Engineering organizations - Demonstrated ability to define architecture standards and guide large engineering teams - Strong stakeholder engagement skills across engineering leadership and executive teams - Self-driven, collaborative, and comfortable working in fast-paced engineering environments - Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or related field - Cloud or AI certifications (Azure Developer, Azure AI Engineer, or equivalent) preferred Benefits - Equal opportunity employer that considers all qualified applicants without regard to age, blood type, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law. - Local employment practices and rights may vary by jurisdiction and are subject to applicable local laws. - Commitment to provide for all those who seek to express interest in employment the opportunity to participate without barriers.

• Construir e evoluir plataformas e serviços internos que abstraem a complexidade de infraestrutura para os times de produto • Escrever software de verdade (não apenas operar ferramentas): automações, integrações e blocos reutilizáveis de plataforma • Trabalhar com infraestrutura como código, containers, Kubernetes e fluxos GitOps • Criar capacidades de self-service, caminhos prontos e a documentação que faz tudo isso ser adotado • Colaborar de perto com o time de Segurança e com as equipes de produto para que a plataforma nasça confiável e adotável • Tomar decisões de arquitetura e sustentar o trade-off técnico junto à liderança