ML Engineer / Data Engineer — LATAM Remote

Description

Job Summary: We are seeking a semi-senior hybrid profile of Data Engineer and MLOps to build and scale the anti-fraud system of a leading fintech in Mexico, working with risk data and models. Key Highlights: 1. Build data pipelines and risk models for a leading fintech. 2. Hybrid role: Data Engineering and MLOps, with direct impact on the product. 3. Collaborate with Data Science and Tech Lead in a strong technical team. Build the data pipelines and risk models for one of Mexico’s largest fintechs. 100% remote from any LATAM country. **Who We Are** We are a leading digital payments infrastructure company in Mexico, serving thousands of clients ranging from startups to enterprises. We have been in production for years and continue to grow. The company name is shared as you advance in the hiring process. Our Engineering team is building and scaling the anti-fraud system that protects every transaction. To achieve this, we need someone who can do both: move data at scale and deploy models into production. **What We’re Looking For** A truly hybrid profile—not a Data Engineer who has never touched a model, nor an MLOps engineer who reached the role without having built pipelines from scratch. Someone who understands both domains and can work directly with a Tech Lead and the Data Science team. The role is semi\-senior: you have your own technical judgment, but you won’t be left alone facing major architectural decisions. **Responsibilities** * Design and maintain risk data ingestion, processing, and storage pipelines (batch and real\-time) * Build the risk model training architecture with weekly automated retraining * Deploy models into production and expose APIs for consumption by the Risk team * Monitor statistical and infrastructure performance of models and pipelines * Collaborate with Data Science to translate their needs into reliable engineering solutions * Document and standardize data definitions for the Risk team **Ideal Profile** * 3\+ years of combined experience in MLOps and Data Engineering, with at least 1 year in each discipline * Strong Python skills in production contexts * Hands-on experience with Airflow or dbt for pipeline orchestration * Fluent SQL, relational and NoSQL databases * Production cloud experience: AWS (Redshift, SageMaker) or GCP (BigQuery, Databricks, Vertex AI) * Kubernetes for service deployment and management * Experience deploying lightweight APIs using Flask or FastAPI **Nice to Have** * Experience building or maintaining a feature store * Familiarity with Spark or Kafka for distributed processing * Experience in anti-fraud systems or credit risk modeling **What We Offer** * Salary: $5,000 USD/month, negotiable based on profile * Work mode: 100% remote from any LATAM country * Project with direct impact on the core product * Technical team led by a Tech Lead who guides without micromanaging * Company with a proven product and real customer base Salary: Starting from $7\.000\.000,00 per month Application Question(s): * Question 1: How many years of experience do you have working with production data pipelines (Airflow, dbt, or similar)? * Question 2: Have you deployed machine learning models into production? Answer yes or no, and in one line state which tool you used. Work Location: Remote position