Production ML Engineer — Build & Deploy Scalable AI

ETAP Software; México; Híbrido; MXN 900.000 - 1.400.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation

Cutting-edge AI/ML and cloud tech

Collaborative, innovative, inclusive

Exposure to global teams

Descripción de la vacante

ETAP Software is seeking a Data Science & Machine Learning Engineer to join its Cloud Division in a hybrid on-site role in Mexico. You will design, build, and deploy intelligent systems at scale, bridging data science and production engineering.

You will work on predictive modeling, graph ML, and generative AI, contributing to product innovation while operationalizing models as scalable services using FastAPI, Docker, and Azure ML.

Formación

• Strong programming skills in Python with Pandas, NumPy and scikit-learn.
• Experience building and deploying end-to-end ML systems in production environments.
• Solid knowledge of SQL/NoSQL databases and data pipeline development.
• Hands-on experience with ML deployment and MLOps tooling.

Responsabilidades

• Design, develop, and deploy machine learning models for classification, regression, ranking, and recommendation systems.
• Build end-to-end ML pipelines from data ingestion to deployment and monitoring.
• Productionize models as scalable APIs using FastAPI, Docker, and cloud platforms (Azure ML).
• Implement robust validation, testing, and monitoring to ensure model performance in production.
• Collaborate with data engineers and software teams to integrate ML solutions into production systems.
• Monitor models for drift, retrain pipelines, and continuously improve performance.

Conocimientos

Python

Pandas

NumPy

scikit-learn

SQL/NoSQL databases

Data pipelines

MLOps

Azure ML

FastAPI

Herramientas

Docker

Git

MLflow

Weights & Biases

PyTorch

Descripción del empleo

ETAP Software is seeking a Data Science & Machine Learning Engineer to join its Cloud Division in a hybrid on-site role in Mexico. You will design, build, and deploy intelligent systems at scale, bridging data science and production engineering.

You will work on predictive modeling, graph ML, and generative AI, contributing to product innovation while operationalizing models as scalable services using FastAPI, Docker, and Azure ML.