ML Ops Staff Engineer

Seniority Level 4:
Specialist / Technical Referent / Team Lead

Workplace Hybrid

Department Technology

dLocal started with one goal – to close the payments innovation gap between global enterprise companies, and customers in emerging economies. We have over 1000 payment methods, in more than 60 countries. 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 merch ants reach billions of underserved consumers in the high-growth markets of Africa, Asia, and Latin America.

dLocal off ers the ideal payment solutions for global commerce.

• Payins:
  Accept local payment methods
• Payouts:
  Compliantly send fund s cross-bor der
• dLocal for Platforms:
  Unify your platform’s payment solution

Financial technology for markets of the future.

We’re looking for an MLOps Technical Referent 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. You will own the technical direction of our MLOps stack, introduce AI safely into our engineering workflows, and help the team scale its impact as usage and complexity grow.

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.

Technical Strategy & Architecture:
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/Fin Ops.

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.

Partner with Fraud, Anomaly, and other product squads to establish clear SLAs/SLOs for ML components, as well as proper logging, metrics, and alerts.

Contribute to on‑call readiness, including playbooks, dashboards, incident reviews, and continuous operational improvement.

AI Infrastructure & AI‑Assisted Operations:
Define infrastructure, contracts, and guardrails to safely consume and extend agents and AI services built by the AI team.

Design patterns and tooling to automate MLOps workflows using AI and agents (e.g., feature platform operations, training/eval pipelines, alert triage in Fraud/Anomaly, and platform Fin Ops/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…