AI Engineer - AI Labs

Why Join dLocal?
dLocal is the financial infrastructure powering global commerce in the world's fastest-growing markets. The biggest companies in the world trust us to unlock growth in 60+ countries across emerging markets—moving money where others see complexity. We don't just process payments; we are architects of payment ecosystems and partners in our customers' expansion. You'll work alongside 1,300+ teammates from 40+ nationalities and tackle global challenges from day one.

You will join the AI Lab
, a team whose mission is to validate high-value emerging AI and automation technologies and de-risk their adoption across dLocal. This is a rare opportunity to work at the frontier of applied AI in fintech: running rigorous experiments on the latest models and tools, and turning results into decisions that shape how a global payments company adopts AI.

This is a senior individual-contributor role. It does not require direct people management, but it carries significant technical influence within the Lab and across the teams that consume its work.

As a Staff AI Engineer in the AI Lab, you own technology scouting, prototyping, and evaluation for dLocal. You will run instrumented spikes and benchmarking on emerging AI technologies, produce clear recommendations for stakeholders across engineering, business, legal and IT based on your prototypes, and coordinate hand-offs to the teams that take validated technologies into production.

For promising technologies, you will help define the patterns, guardrails, and technical requirements needed for adoption and coordinate the hand-off to the engineering teams responsible for product ionizing them.

• Run short, instrumented spikes and benchmarking on new models, tools and frameworks: LLMs, agentic systems, vector databases, orchestration frameworks, copilots, assistants and more.
• Compare vendor and open-source options, documenting trade-offs across quality, cost, latency, security and integration complexity.
• Deliver concise decision memos with clear recommendations: adopt, watch, or avoid.

• Design and maintain evaluation environments (e.g. datasets, prompts, scenarios, telemetry) to test models under realistic constraints.
• Build automation and tooling to measure quality, robustness, latency and cost, including regression tracking over time.
• Ensure every evaluated technology has benchmark coverage and a documented risk and limitations view.

• Build enough of a system to understand how a technology behaves under realistic conditions, not just in vendor demos or isolated examples.
• Explore architecture, integration patterns, operational constraints, security boundaries, and failure modes through working prototypes.
• Determine what must be true for a proof of concept to become a viable production capability.
• Prefer focused prototypes that answer specific technical questions over prematurely building production systems.

• Translate technical findings into clear decision memos for both technical and non-technical stakeholders.
• For validated technologies, produce readiness guidance covering recommended patterns, guardrails, known limitations, operational considerations, and integration requirements.
• Coordinate hand-offs to the engineering teams responsible for productionization.
• Support those teams during the transition when deep context from the evaluation is required, without becoming the permanent owner of the resulting system.
• Track what happens after Lab recommendations and use those outcomes to improve future evaluation methods.

• Work with Security, Legal, Compliance and other AI teams to document risk assessments, mitigations and governance recommendations for each evaluated technology.
• Maintain checklists, decision templates and lightweight standards reusable across evaluations and by partner teams.
• Incorporate learnings from third-party AI tooling already in use, such as external copilots and the AWS AI suite, into adoption guidelines.

• Partner with other AI teams and domain teams to ensure clear boundaries and smooth collaboration.
• Participate in hiring as a technical evaluator and culture champion.
• Mentor engineers in the Lab and adjacent teams on evaluation methods, benchmarking and experimental design.
• Share knowledge through internal write-ups, tech talks and…