Senior ML/AI Engineer with GCP

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

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Client

Our client is a leading airline in Latin America, operating the region's largest network of destinations, flight frequencies, and fleet. The company is driving innovation through advanced AI and Machine Learning initiatives, with a strong focus on next generation Generative AI solutions.

Join a great company, not merely an individual project

Project overview

You will join a strategic initiative within the Emantto domain, contributing to the acceleration of the organization's AI portfolio across multiple business areas. This role combines ML/AI engineering with strong software engineering, cloud, and infrastructure expertise. You will build and operate Generative AI driven data products, support domain teams, and act as a facilitator for the Data & AI Platform.

Position overview

We are looking for an ML/AI Engineer with solid knowledge of cloud technologies, Infrastructure as Code (IaC), CI/CD practices, and software engineering best practices, focused on designing, building, and operationalizing Generative AI based solutions.

Responsibilities

• Develop and deliver data products and AI/Generative AI solutions within domain teams.
• Act as a facilitator of the Data & AI Platform, enabling adoption and accelerating delivery across teams.
• Build, deploy, and operate Generative AI and Machine Learning models in scalable, production ready environments.
• Manage infrastructure related aspects of environments and AI/ML products, including observability, performance, and reliability.
• Contribute to CI/CD pipelines, Infrastructure as Code practices, and platform automation initiatives.
• Collaborate with cross functional teams, including Software Engineering, Data Engineering, MLOps, and DevOps teams, to maintain high engineering standards.
• Support experimentation frameworks and internal tools for Generative AI model development and evaluation.

Requirements

• Experience working with Google Cloud Platform (GCP).
• Experience with Terraform or other Infrastructure as Code tools.
• Strong proficiency in Python.
• Experience in backend engineering, including APIs and services, as well as Generative AI, Machine Learning, or MLOps technologies such as Airflow, MLflow, pipelines, and monitoring tools.
• Solid understanding of CI/CD practices, containerization using Docker, and software engineering best practices.
• Familiarity with model deployment, model serving, and operating Machine Learning and AI systems in production environments.

Nice to have

• Experience with observability, incident response, or platform operations.