dLocal started with one goal – to close the payments innovation gap between general enterprise companies, and customers in emerging economies. We are relentlessly focusing on serving our customers and solidifying our position as the preferred infrastructure solution for Global Merchants across Emerging Markets. With the ability to accept local payment methods and facilitate cross-border fund settlement worldwide, our merchants reach billions of underserved consumers in the high-growth markets of Africa, Asia, and Latin America. dLocal offers the ideal payment solutions for global commerce. Payins: Accept local payment methods Financial technology for markets of the future. We’re looking for an Tech Lead
- AI and ML Infrastructure to serve as the senior technical reference for how we build, operate, and evolve our ML and AI infrastructure. Your mission is to enable Data Science and AI teams to take models and AI-powered services from idea to production in a reliable, observable, and compliant way. A core part of this role is to use agents and AI services to automate as much as possible of what we do in MLOps — from feature store and platform operations to fraud/anomaly workflows and ML cost optimization — working side by side with the AI team. This is a hands-on architecture and leadership role: you won’t own product models yourself, but you will deeply influence how every model and AI component is trained, deployed, monitored, and run in production. Define and evolve the end-to-end ML platform architecture (data, training, registry, serving, monitoring, governance) used by multiple squads.



Design standard patterns for reproducible training pipelines, experiment tracking, model packaging, versioning, promotion flows (dev → staging → production), and online/batch inference with safe rollout strategies (canary, shadow, rollback). Balance reliability, performance, and cost for ML workloads, working closely with SRE/Infra and Finance/FinOps. Day-to-Day MLOps Enablement: Act as the go-to expert for complex MLOps questions regarding pipeline structure, serving patterns, monitoring, and rollbacks. Review and challenge designs and deployments for new models and data pipelines, ensuring adherence to platform standards and non-functional requirements. feature platform operations, training/eval pipelines, alert triage in Fraud/Anomaly, and platform FinOps/cost optimization). Contribute to evaluation, observability, and safety for AI-powered automations (prompts, policies, redaction, auditability) alongside dedicated AI teams. Governance, Security & Compliance: Set and maintain technical standards for model/data access control, PII handling, redaction, environment separation, and auditability of model changes and runtime behavior. Mentor MLOps and Data/ML engineers on system design, reliability,



observability, CI/CD, testing, and rollback practices. Lead design and architecture reviews to de-risk decisions and collaborate closely with Data Science, AI, SRE/Infra, and Product/Engineering leaders. Strong background in distributed systems and data/stream processing (e.g., Spark, Flink, or similar technologies). Experience building production-grade ML pipelines, including experiment tracking, reproducible training, model registries, CI/CD for models/data, and online/batch inference at scale. Strong hands-on experience building and operating production ML workloads on Databricks, including feature engineering/Feature Store (ideally with Unity Catalog), MLflow experiment tracking and model lifecycle management, and batch/real-time model deployment. Familiarity with cloud-based ML platforms (e.g., SageMaker, Vertex AI) and container-based deployments. Strong understanding of observability for ML systems, including metrics, logs, traces, data/model drift, freshness, and quality checks. Experience rolling out AI assistants (code or infra copilots, AI log analysis) inside engineering organizations, including policies and best practices, is a plus. Prior responsibilities as a Technical Referent, Tech Lead, or Architect for platforms or shared services is a plus. Contributions to internal standards, RFCs, guilds, or tech communities is a plus. You’ll partner with Data Science, AI, SRE, Infra, and Product leadership to make our ML and AI platforms easier to operate, scalable, and safe to evolve.

📌 Unity Tech Lead (España)
🏢 dLocal
📍 España