Senior ML/AI Engineer with GCP

Job description

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.

Project

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

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.

Additional requirements

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