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Staff AI Software Engineer

🏢 Kueski · Guadalajara, Mexico · Remoto · publicado 23/09/2026

ABOUT KUESKI At Kueski, we're dedicated to improving the financial lives of people in Mexico. Since 2012, we've been the leading buy now, pay later (BNPL) and online consumer credit platform in Latin America, known for our innovative financial services. Our flagship product, Kueski Pay, provides seamless payment solutions for both online and in-store transactions, establishing itself as the preferred option for nearly 30% of Mexico's top e-commerce merchants. Notably, we were the first to introduce BNPL on Amazon Mexico. We're a tech company with a culture geared toward innovation, collaboration, and impact, fostering a strong, diverse, and inclusive workplace. Our commitment to excellence and ethical business practices has earned us multiple industry recognitions. In 2024, we were named one of the World’s Top FinTech Companies by CNBC and recognized as one of the most ethical companies in Mexico by AMITAI. Additionally, we were certified as a Best Place to Work for LGBTQ+ Equality by HRC Equidad MX 2025 and ranked among the Best Companies for Female Talent by EFY. POSITION Kueski is seeking a Staff AI Software Engineer to join the AI Enabling Team. This role is ideal for a hands-on engineer who will build harnesses, developer tools, reusable workflows, and guardrails that enable Engineering and Product teams to use AI agents effectively at scale. JOB REQUIREMENTS - Experience building and shipping AI products, combined with a strong perspective on transforming legacy organizations into AI-native ones. - 10+ years building software, with several spent designing and shipping ML/AI systems in production at scale. - Strong systems and software engineering fundamentals. - A track record of Staff+ technical influence, setting direction that multiple teams follow, and being trusted to make high-ambiguity calls with incomplete information. - Sharp judgment on the build/buy/fine-tune/prompt spectrum, and the discipline to optimize for outcomes and cost
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