ML Ops Staff Engineer

Nodi; Barcelona; Híbrido; EUR 80.000 - 110.000; Jornada completa

Descripción de la vacante

dLocal in Barcelona is seeking an MLOps Technical Referent to drive the end-to-end ML platform, from data to production, with focus on reliability, observability, and governance. You will lead architectural decisions, onboard agents, and enable Data Science and AI teams to scale models safely across the organization.

You will partner with Data Science, AI, SRE, Infra, and Product leaders to balance hands-on delivery with standards and enablement across squads.

Formación

• Experience owning or designing MLOps platforms used by multiple teams.
• Built production ML pipelines with experiment tracking and model registries.
• Strong experience with Databricks and MLflow.
• Knowledge of cloud-based ML platforms and container deployments.
• Ability to communicate technical concepts to non-technical stakeholders.

Responsabilidades

• Define end-to-end ML platform architecture.
• Design reproducible training pipelines, experiment tracking, and model versioning.
• Balance reliability, performance, and cost for ML workloads.
• Lead MLOps enablement and respond to complex questions.
• Review designs and deployments for new models and data pipelines.
• Partner with fraud/anomaly teams to set SLAs/SLOs for ML components.
• Contribute to on-call readiness, playbooks, and incident reviews.
• Define infra and guardrails to safely use agents and AI services.

Conocimientos

MLOps Platform

Distributed Systems

ML Pipelines

Databricks

Unity Catalog

Model CI/CD

Cloud ML Platforms

Observability

Stakeholder Communication

LLM Infra

Herramientas

Spark

Flink

MLflow

Unity Catalog

Databricks

SageMaker

Vertex AI

Kubernetes

Descripción del empleo

Seniority Level 4: Specialist / Technical Referent / Team Lead

Workplace Hybrid

Department Technology

About dLocal

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.

What will I be doing?

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/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.

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 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.

Work with InfoSec and Architecture to align the platform with regulatory and internal requirements.

Leadership & Collaboration: 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.

What skills do I need?

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, 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.

Ability to communicate clearly with technical and non‑technical stakeholders, translating infrastructure and AI/ML trade‑offs into business language.

Experience rolling out AI assistants (code or infra copilots, AI log analysis) inside engineering organizations, including policies and best practices, is a plus.

Exposure to LLM and AI infrastructure (gateways, vector stores, evaluation harnesses) 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.

How you’ll work

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. You’ll balance hands‑on architecture and engineering delivery with clear technical standards, operational excellence, and continuous enablement across squads.