TL;DR

MLOps & AI Infrastructure Technical Referent (AI Engineering): Defining and evolving the ML platform architecture and enabling Data Science and AI teams to deploy models and AI-powered services to production reliably. Focus on AI-assisted operations, governance, security, and compliance within the ML/AI infrastructure.

Location: Madrid / Barcelona

Company

dLocal enables global companies to collect payments in 40 emerging markets, increasing conversion rates and simplifying payment expansion.

What you will do

  • Define and evolve the end-to-end ML platform architecture, covering data, training, registry, serving, monitoring, and governance.
  • Design standard patterns for reproducible training pipelines, model packaging, versioning, and safe rollout strategies.
  • Act as the go-to person for complex MLOps questions and review designs for new models and data pipelines.
  • Define infrastructure and tooling for AI-assisted operations in MLOps, such as feature platform operations and operational flows in fraud/anomaly detection.
  • Set and maintain technical standards for model and data access control, PII handling, and auditability.
  • Mentor MLOps and Data/ML engineers on system design, reliability, and observability.

Requirements

  • Solid experience owning or designing MLOps platforms or ML infrastructure used by multiple teams.
  • Strong background in distributed systems and data/stream processing (e.g. Spark, Flink).
  • Experience building production-grade ML pipelines, including experiment tracking, CI/CD for models, and online/batch inference at scale.
  • Familiarity with cloud-based ML platforms (e.g. Databricks, SageMaker, Vertex AI) and container-based deployments.
  • Strong understanding of observability for ML systems, including metrics, logs, traces, and data/model drift checks.
  • Ability to communicate clearly with both technical and non-technical stakeholders.

Nice to have

  • Experience rolling out AI assistants inside engineering organizations, including policies and best practices.
  • Exposure to LLM and AI infrastructure.
  • Prior responsibilities as Technical Referent / Tech Lead / Architect for platforms or shared services.
  • Contributions to internal standards, RFCs, guilds or tech communities.

Culture & Benefits

  • Flexible schedules and a focus on performance and impact.
  • Work in a dynamic Fintech environment with opportunities to build and boost creativity.
  • Access to a Premium Coursera subscription for learning and development.
  • Free English, Spanish, or Portuguese classes.
  • Monthly social budget for team activities.
  • Opportunity to rent a dLocal House to spend a week co-working with your team anywhere in the world.

Hiring process

  • The Talent Acquisition team is invested in creating the best candidate experience possible.
  • CVs will be reviewed and candidates will be kept posted by email at every step of the process.