Senior GCP AI Engineer - Generative AI & MLOps

DataArt; Monterrey; Presencial; MXN 600.000 - 900.000; Jornada completa

Descripción de la vacante

DataArt is partnering with a leading Latin American airline to accelerate its Generative AI initiatives. You will design, build, and operate AI/ML data products, collaborate with software, data engineering, and MLOps teams to drive scalable AI solutions.

The role emphasizes GCP, Terraform/IaC, Python, and production-grade AI systems, with a focus on observability, CI/CD, and platform automation to enable rapid AI-driven outcomes across business domains.

Formación

• Experience with Google Cloud Platform (GCP)
• Terraform or other Infrastructure as Code tools
• Strong proficiency in Python
• Backend engineering including APIs/services and Generative AI, ML, or MLOps tech (Airflow/MLflow/pipelines/monitoring)
• Solid CI/CD practices, Docker, and software engineering
• Familiarity with model deployment, serving, and production ML/AI systems

Responsabilidades

• Develop and deliver Generative AI and ML data products within domain teams
• Facilitate Data & AI Platform adoption and acceleration
• Build, deploy, and operate Generative AI/ML models in production environments
• Manage infrastructure aspects of environments and AI/ML products (observability, performance, reliability)
• Contribute to CI/CD pipelines and IaC practices
• Collaborate across Software, Data, MLOps, and DevOps teams to maintain high engineering standards
• Support experimentation frameworks and internal tools for Generative AI model development

Conocimientos

GCP

Terraform

Python

Backend engineering

CI/CD

Docker

MLOps

Model deployment

AI tooling

Herramientas

Airflow

MLflow

Pipelines

Monitoring tools

Descripción del empleo

DataArt is partnering with a leading Latin American airline to accelerate its Generative AI initiatives. You will design, build, and operate AI/ML data products, collaborate with software, data engineering, and MLOps teams to drive scalable AI solutions.

The role emphasizes GCP, Terraform/IaC, Python, and production-grade AI systems, with a focus on observability, CI/CD, and platform automation to enable rapid AI-driven outcomes across business domains.