• Strategic Account Ownership: Own and grow strategic customer relationships, acting as the primary point of contact for executive stakeholders and technical teams alike. • Drive AI Infrastructure Revenue: Accelerate the adoption of Vultr’s AI cloud infrastructure solutions by identifying opportunities and guiding customers through the sales cycle—from initial engagement through solution architecture and scale-up. • Customer-Centric Engagement: Understand customer priorities, technical requirements, and business goals to position Vultr’s value proposition effectively and deliver tailored solutions. • Trusted Advisor: Provide thought leadership on AI/ML trends, infrastructure needs, and optimization strategies to senior leaders within your accounts. Help customers navigate the AI landscape and make informed architectural decisions. • Collaborate for Success: Work cross-functionally with Product Management, Solutions Engineering, and Customer Success to ensure alignment on product capabilities, roadmap feedback, and long-term success. • Sales Process Excellence & Operational Hygiene: Document and maintain accurate sales activity data in CRM tools to support MRR forecast accuracy. • AI Ecosystem Engagement: Understand and leverage the AI partner ecosystem to enhance Vultr’s value proposition for Founders and C-suite executives.

• Diseñar y desarrollar soluciones de Inteligencia Artificial listas para producción utilizando modelos tradicionales y Generative AI. • Liderar técnicamente la implementación de arquitecturas basadas en LLMs, RAG y agentes inteligentes. • Diseñar APIs, microservicios y componentes reutilizables para acelerar nuevas iniciativas de IA. • Integrar modelos de IA con sistemas corporativos, plataformas cloud y fuentes de datos estructurados y no estructurados. • Diseñar arquitecturas escalables sobre Azure, AWS o GCP considerando disponibilidad, seguridad y mantenibilidad. • Implementar prácticas de MLOps y LLMOps para automatizar despliegues, versionamiento, monitoreo y operación de modelos. • Diseñar e implementar pipelines de procesamiento de datos (ETL/ELT) para soluciones de IA. • Definir estrategias de evaluación, observabilidad y monitoreo del desempeño de modelos y agentes. • Colaborar con equipos de negocio para transformar requerimientos funcionales en soluciones técnicas de alto impacto. • Promover buenas prácticas de arquitectura, calidad, seguridad y gobernanza dentro de las iniciativas de Inteligencia Artificial. • Implementar soluciones de IA utilizadas en producción con impacto medible en el negocio. • Reducir el tiempo de desarrollo e implementación mediante arquitecturas reutilizables. • Diseñar componentes escalables que aceleren futuras iniciativas de IA. • Garantizar soluciones con alta disponibilidad, observabilidad y desempeño. • Mejorar continuamente la calidad y precisión de modelos y agentes desplegados. • Cumplir con estándares de seguridad, gobernanza y buenas prácticas de ingeniería.

• Lead AI/LLM strategy, solution architecture, and implementation across Engineering, Operations, and Project Delivery. • Build and maintain LLM-based agents to support: intelligent processing of technical documentation, automated design validation and engineering workflows, testing and QA automation, knowledge retrieval and contextual reasoning. • Integrate AI into core power automation workflows: IEC 61850 SCD engineering files, relay settings, SCADA HMI & logic, substation documentation, etc. • Establish AI governance, secure data pipelines, and compliance with utility-grade cybersecurity standards. • Partner with engineering managers and subject-matter experts to identify high-value AI automation opportunities. • Develop scalable pipelines for inference, fine-tuning, continuous learning, and lifecycle management in cloud and on-prem environments. • Evaluate and incorporate emerging AI technologies (RAG, vector stores, autonomous agents, internal copilots). • Monitor model performance, accuracy, drift, and cost; lead improvement cycles and risk mitigation. • Train and coach engineering teams on practical AI tools and adoption in daily workflows. • Ensure compliance with GE Vernova global standards, regulatory expectations, and utility-sector requirements.

• Join an existing development team to build and ship LLM-powered features in a complex, large-scale production application. • This is a hands-on, full-stack role spanning backend services, APIs, and the LLM systems (retrieval, agents, and evaluation). • Leverage AI coding tools and autonomous agents to write code, automate workflows, and optimize delivery. • Work closely with globally distributed teams across multiple time zones, owning features end-to-end. • Bring senior-level expertise in Python and LLM engineering, plan, execute, and deliver technical solutions.