• Own 0-to-1 development of new products and features – from problem discovery to launch to iteration. • Define what to build, why, and how to measure success – then see it through. • Work directly with users, support, and data to understand real problems, not assumed ones. • Run experiments, define metrics, and shut down what doesn't work. • Collaborate deeply with engineering – understand architectural trade-offs, review technical approaches, and unblock decisions. • Prototype and validate ideas fast using AI tools (Claude, Cursor, Copilot, agents), scripts, no-code, or whatever gets you to the answer quickest. • Identify where AI and LLMs can create real product value – and build the case for it. • Write product specs and briefs that engineers actually want to read – clear, technical, and opinionated. • Stay hands-on: write queries, dig into data pipelines, spin up prototypes, open a PR when it makes sense.

• Implement ANet’s next generation of technology products that support data-driven educational practices • Design, develop, test, and maintain a robust and scalable data lake solution, RESTful back-end services, and, occasionally, web applications that provide ANet’s educational partners with insights that boost student learning • Engage in technical design and code reviews, embracing and promoting best practices to document and deliver a high-quality software system that scales • Monitor and troubleshoot production data pipelines, proactively address issues, performance bottlenecks, and system failures • Contribute to the evolution of our data infrastructure, recommending and implementing solutions to improve testability, maintainability, scalability, and efficiency • Join product managers and business analysts to understand available data and translate user stories into reporting platform capabilities • Collaborate and integrate with third-party data providers to ingest and share data using established, sustainable patterns adopted by the team • Keep abreast of new technologies and propose adoption as appropriate

• Develop and maintain software platforms and infrastructures that support multiple engineering teams; • Design, implement, and evolve CI/CD pipelines, ensuring efficiency, reliability, and quality in delivery processes; • Develop and maintain scalable, high-performance, and secure fullstack solutions; • Work closely with product teams to understand requirements and define technical solutions; • Implement and evolve automated testing strategies (unit, integration, end-to-end, performance, and security); • Automate operational processes and development workflows to increase productivity and optimize costs; • Monitor systems and platforms, analyzing metrics, SLIs, SLOs, and SLAs, and proposing continuous improvements; • Operate with a systemic perspective, identifying risks, bottlenecks, and opportunities within the technology ecosystem; • Provide technical support to the team, promoting best practices, standardization, and professional development; • Evaluate and propose technical solutions aligned with strategic objectives, OKRs, and KPIs.

• Liderar a estratégia de engenharia para soluções e plataforma de IA (arquitetura, padrões, roadmaps técnicos e guias de adoção). • Definir e manter padrões e boas práticas para desenvolvimento, testes e operação de aplicações com IA (APIs, SDKs, templates, bibliotecas e aceleradores). • Acompanhar e impulsionar a evolução técnica dos times (revisão de arquitetura/código, decisões e trade-offs). • Atuar na solução de problemas complexos em ambientes distribuídos. • Ser referência técnica em engenharia aplicada a IA: model serving, integração com LLMs, RAG, pipelines de dados/features, governança e segurança. • Acompanhar e evoluir métricas de engenharia e de IA, como: SLO/SLI, latência, taxa de erro, custo por requisição, qualidade de resposta, alucinação, groundedness, cobertura de testes e taxa de incidentes. • Colaborar no design e desenvolvimento de ferramentas e serviços da plataforma com foco em segurança, performance e observabilidade (logs/métricas/traces, tracing distribuído, correlação ponta a ponta). • Garantir operação e sustentação em produção: monitoramento e alertas, participação em war rooms, pós-incidente (RCA), melhorias contínuas e gestão de riscos. • Assegurar escalabilidade, resiliência e consistência das soluções de IA: controles de rate limit, fallback/degradação, cache, versionamento de prompts/modelos, segurança de dados e guardrails. • Contribuir continuamente com ideias e propostas para enriquecer o roadmap (redução de custo/latência, melhoria de qualidade, automação de avaliação, padronização de integrações).