• Partner Across the Organization • Work closely with Data and business teams to understand workflows, identify pain points, and find high-impact opportunities for AI-driven automation and efficiency. • Turn business problems into clear AI use cases. • Design and iterate on practical AI tools such as copilots, agents, and automated workflows using modern GenAI techniques. Write clean, reliable code in Python and Golang to support these solutions. • Partner with engineering and data teams to turn prototypes into secure, scalable, production-ready systems. • Guide teams on AI capabilities, responsible use, and best practices. Mentor others on design, coding standards, and effective AI tools. • Pilot new solutions with teams, gather feedback, iterate quickly, and support rollout to ensure real business impact. • Work with Security, Legal, and Compliance to meet privacy, security, and ethical standards. • Define success metrics and track outcomes such as time saved, cost reduction, increased speed and productivity, revenue impact, risk reduction. • Continuously explore new AI models, tools, and practices to bring practical innovations into the business.

• Define and own end-to-end AI architecture for multiple enterprise use cases, including data flows, model lifecycle, and serving patterns across Azure and AWS. • Design AI/ML solution blueprints covering data ingestion, feature stores, training pipelines, model registry, deployment, monitoring, retraining, and decommissioning. • Establish and standardize MLOps frameworks (MLflow/Kubeflow/Airflow, Docker, Kubernetes) and reference implementations that AI/ML engineers and data scientists can reuse across teams. • Collaborate with product, data, and business stakeholders to identify AI opportunities, shape solution options, and align AI architectures with business and non-functional requirements (scalability, reliability, cost, latency). • Define and enforce governance for model approval, explainability, versioning, drift monitoring, and compliance with data privacy and regulatory requirements relevant to the organization. • Guide the selection and integration of cloud-native services (Azure ML, AWS SageMaker, Lambda, EC2, S3, Azure Functions, API gateways, monitoring stacks) into cohesive AI platforms. • Work closely with security, compliance, data, and enterprise architecture teams to ensure AI solutions meet standards for security, resilience, observability, and cost optimisation. • Provide technical leadership and mentoring to AI engineers, ML engineers, and data scientists on best practices in AI architecture, MLOps, and cloud-native design. • Create and maintain architectural artefacts: high-level designs, detailed solution diagrams, standards, and documentation for AI platforms and reference solutions.

• Define the technical vision and architecture for AI-orchestrated developer workflows at Canva • Establish new engineering paradigms around AI tooling, agent orchestration, and context engineering • Architect foundational AI systems and frameworks that become the platform every engineer at Canva relies on • Drive industry-leading practices in agentic AI development and enterprise-scale AI tooling adoption • Shape and mentor engineers joining the expanded team through technical leadership and vision-setting • Solve challenges no one's solved before with access to state-of-the-art tooling, models, and infrastructure • Define and architect our enterprise-level strategic vision and technical roadmap for AI-orchestrated developer workflows at Canva • Pioneer new AI engineering practices by researching and developing high-value AI methodologies, frameworks, and technologies that go beyond code generation to transform all parts of the Canva SDLC • Drive paradigm shift in how engineering teams think about and adopt AI tooling, establishing patterns and practices that influence the broader industry • Keep Canva at the bleeding edge through deep industry research, experimentation with emerging AI technologies, and strategic evaluation of new paradigms (MCPs, Skills, multi-agent frameworks)

• Experiencia depslegando y programando soluciones basadas en agentes inteligentes y LLMs, integrando herramientas como LangChain, LlamaIndex, AutoGen, CrewAI o frameworks equivalentes. • Implementar agentes que interactúan con usuarios, APIs, sistemas ERP o plataformas externas (e.g. WhatsApp, sistemas contables, CRMs). • Implementar arquitecturas MCP (Model Context Protocol) y A2A (Agent-to-Agent) para permitir coordinación multi-agente y flujos autónomos dentro de entornos empresariales. • Trabajar en ambientes productivos y con interacción directa con equipos de infraestructura, DevOps y MLOps • Colaborar estrechamente con equipos de producto, UX, datos y backend para tener un delivery exitoso de las funcionalidades planeadas. • Implementar con buenas prácticas de desarrollo, validación y monitoreo de agentes, incluyendo tests de integración, control de versiones de prompts y trazabilidad de decisiones. • Mantenerse actualizado en frameworks de código abierto y papers relevantes en el campo de agent frameworks, memory architectures y multi-agent systems.