Staff AI Software Engineer

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.

Clear communicator who can align engineers, PMs, and risk/compliance stakeholders — and give a direct technical opinion when it matters.

Key Responsabilities

AI-Native Foundations

We're not starting from a blank page, you'll evolve Kueski's existing services, data platforms, and pipelines into systems that AI agents and models can operate on safely and effectively. Decide what to wrap, refactor, expose, or retire to make our infrastructure legible and actionable to AI, and sequence that transformation without a rewrite-the-world reset.

Make Kueski's knowledge (code, data, business logic, docs, and operational state) structured and retrievable so LLMs and agents can reason reliably over our real systems. Own the retrieval, memory, and knowledge-representation layer that turns scattered institutional context into something models can actually use.

AI-Native Engineering Workflows

Redefine how we build (specs, code, review, testing, deployment) around agentic engineering workflows. Set the standards, tooling, and guardrails that make AI a default part of the development lifecycle rather than a side experiment.

Production Systems, Evaluation & Guardrails

Design and ship the agentic systems behind our internal workflows, and take at least one high-stakes AI capability all the way to production, proving the impact with data.

Establish the eval harnesses, observability, and safety/compliance guardrails that make AI trustworthy in a regulated lending environment: PII handling, model risk, and auditability.

Organizational Leverage

Build reusable platforms and reference implementations, raise the engineering org's AI-native fluency through direct collaboration and mentorship, and turn one-off wins into self-sustaining capability.