Senior AIOps Engineer

🇪🇸 Madrid, ES Sur site IT Publié Avr 13, 2026

Postuler

LieuMadrid, ES

Mode de travailSur site

CatégorieIT

Catégorie ITData Science et ML

LangueEnglish

Publié13 avril 2026

Dernière vérification8 mai 2026

Why should you join dLocal? dLocal enables the biggest companies in the world to collect payments in 40 countries in emerging markets. Global brands rely on us to increase conversion rates and simplify payment expansion effortlessly. As both a payments processor and a merchant of record where we operate, we make it possible for our merchants to make inroads into the world’s fastest-growing, emerging markets. By joining us you will be a part of an amazing global team that makes it all happen. Being a part of dLocal means working with 1000+ teammates from 30+ different nationalities and developing an international career that impacts millions of people’s daily lives. We are builders, we never run from a challenge, we are customer-centric, and if this sounds like you, we know you will thrive in our team. What's the Opportunity? The AI Ops Engineer is a highly technical role responsible for the reliability, scalability, and continuous improvement of dLocal’s AI ecosystem across the entire organization. As AI adoption expands company-wide, we need a hands-on expert who can operate, maintain, evaluate, and evolve our AI systems in production—ensuring consistent quality, robustness, security, and measurable business impact for teams across dLocal. In this role, you will act as both an enabler and a technical authority. You will work alongside Engineering and cross-functional teams to ensure our AI systems—built both in-house and leveraging best-in-class third-party tools—operate at the highest standards of performance and efficiency. You will maintain and customize AI-powered information systems, including prompt libraries, skills frameworks, orchestration layers, integrations, evaluation pipelines, and observability tooling. This position requires strong analytical capabilities, deep systems thinking, and the ability to rigorously compare models, architectures, and tools to consistently achieve the best possible outcomes