• Arquitectura y despliegue de infraestructura cloud • Automatización de despliegues • Integración de observabilidad/búsqueda como OpenSearch • Optimización de costos y operación en nube • Experiencia en configuracion de ambientes LINUX

• Lead the design and enhancement of automation frameworks and CI/CD pipelines, setting standards for excellence. • Architect and manage complex cloud environments, focusing on performance, security, and scalability. • Serve as a mentor to DevOps Engineers, fostering a culture of continuous improvement and best practices. • Tackle complex system performance issues, employing advanced analytical skills to maintain reliability and efficiency.

• Monitor API connections and integrations with supply partners, proactively identifying issues with data quality, response accuracy, or operational workflows. • Diagnose, triage, and resolve real-time and recurring problems, including undocumented errors, data discrepancies, and rate limiting behaviors. • Partner with Engineering to QA new supplier integrations and system changes before and after deployment, including reviewing API behavior, validating fix effectiveness, and surfacing edge cases that automated testing may miss. • Collaborate cross-functionally with Product, Engineering, and Supply teams to align on impact, prioritize high-severity incidents, and expedite resolution. • Lead or participate in incident management, investigating root causes and ensuring all relevant details (logs, error messages, impacted workflows) are documented in tracking systems. • Maintain and update documentation, knowledge bases, and standard operating procedures to drive efficiency and support continuous improvement. • Develop and track incident metrics (e.g., response times, resolution SLAs, recurring issue types) to inform supplier performance reviews and system enhancements. • Assist with ongoing monitoring, reporting, and process optimization to reduce incident frequency and improve platform reliability.

• Design and evolve multi-provider, multi-region GPU compute clusters optimized for large-scale training • Serve as the primary technical point of contact for customers running large-scale training workloads • Define SLOs and error budgets that account for the unique failure modes of GPU infrastructure • Ensure the health and performance of high-speed interconnects • Build deep visibility into GPU utilization, memory pressure, interconnect throughput • Build production-grade automation for cluster provisioning, GPU health checks, job scheduling • Lead incident response for complex failures spanning hardware, networking, orchestration