Principal Software Engineer at Sabre in Bengaluru

Job description

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. Role Summary The Principal Software Engineer is a highly technical, hands-on engineering leader responsible for defining architecture, solving complex engineering problems, and raising technical standards across teams. The role establishes AI as a foundational engineering capability across the Software Development Lifecycle (SDLC), using approved AI coding assistants, engineering copilots, autonomous agents, and modern developer platforms to improve delivery speed, software quality, developer experience, and operational outcomes while maintaining security, reliability, compliance, and intellectual property safeguards. The ideal candidate combines deep software engineering expertise with strong architectural judgment, broad organizational influence, and an AI-first mindset. They remain close to code and implementation, lead through technical decisions and reusable patterns, mentor senior engineers and technical leads, and convert business and customer needs into scalable, secure, resilient, and maintainable solutions. Key Responsibilities AI-First Engineering Transformation · Champion AI-first engineering practices across requirements analysis, architecture, design, coding, code review, testing, documentation, deployment, operations, incident response, and support. · Identify and implement opportunities to automate repetitive engineering, testing, documentation, troubleshooting, and operational work using approved AI capabilities. · Lead technical adoption of AI-assisted development platforms, engineering copilots, autonomous agents, and AI-enabled developer workflows. · Create reusable prompts, agent patterns, implementation of blueprints, reference solutions, and playbooks that enable effective and consistent AI adoption across teams. · Use measurable engineering outcomes to evaluate AI adoption, including cycle time, throughput, deployment frequency, defect trends, automation coverage, maintainability, and developer effectiveness. Architecture and Technical Leadership · Develop comprehensive software designs, system architecture diagrams, engineering specifications, decision records, and implementation roadmaps aligned with business, customer, product, and stakeholder objectives. · Design and implement new components and subsystems, and review or modernize existing architectures when required. · Provide hands-on leadership through architecture reviews, design reviews, code reviews, technology evaluations, prototypes, and critical implementation work. · Guide teams in the appropriate use of Generative AI services, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, Model Context Protocol (MCP) servers, and emerging AI technologies. · Ensure solutions meet enterprise expectations for scalability, performance, availability, fault tolerance, maintainability, observability, extensibility, data privacy, and security. · Define consistent product and system patterns that improve quality, integration, reuse, and cross-team collaboration. AI Governance, Quality and Responsible Engineering · Establish practical engineering standards and validation controls for AI-generated code, tests, designs, documentation, and operational artifacts. · Ensure AI-assisted solutions comply with enterprise governance, security, privacy, risk management, licensing, intellectual property, and regulatory requirements. · Require hum