SRE Software Systems Engineer IV

Powering the agentic revolution in travel.

Sabre is an AI-native technology leader, backed by one of the world’s largest travel data clouds.

Built on an open, modular, cloud-native architecture, Sabre serves as the backbone for both established leaders and bold, new disruptors, guiding them to the next age of travel retailing through intelligent, connected, and personalized experiences.

With AI at its core and operating at unparalleled scale, Sabre transforms insights into innovation, empowering airlines, hoteliers, agencies and other partners to retail, distribute and fulfill travel worldwide.

SRE Software Systems Engineer IV - Data Intelligence and AI Operations

We are seeking a SRE Software Systems Engineer to join our global Data Intelligence and AI Operations team, where innovation knows no borders.

This team builds high-throughput data pipelines and artificial intelligence platforms that power smarter travel decisions worldwide.

It connects core Google Cloud Platform infrastructure with advanced machine learning workloads to scale operations with speed and precision.

With an inclusive culture and flexible work environment, we work together with boldness, curiosity and commitment so we can all win together.

As a SRE Software Systems Engineer, you will drive platform reliability, auto-scaling and cloud cost efficiency across our next-generation data platforms.

You will eliminate operational toil, optimize Google Kubernetes Engine environments and build resilient automation to keep mission-critical applications running smoothly.

This role requires strong Site Reliability Engineering discipline, problem-solving skills and the confidence to challenge outdated processes.

Working schedule

The role will require on call rotation and working after normal US working hours and some weekends.

Modularize and manage core Google Cloud Platform services using Infrastructure as Code to ensure strict security and scalability.

Optimize resource utilization, auto-scaling policies and cloud spend for large-scale data and machine learning workloads.

Operationalize machine learning models and Retrieval-Augmented Generation architectures using robust continuous integration pipelines.