Sabre
From Sabre's careers page
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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.
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 human accountability for critical technical decisions and validate AI-generated output through code review, automated testing, security scanning, performance testing, and production readiness checks.
Balance rapid experimentation and innovation with engineering discipline, architectural integrity, operational reliability, and responsible AI practices.
Engineering Excellence and Delivery
Drive technical execution of complex, high-impact initiatives across multiple teams and geographies, managing technical risks, dependencies, and integration concerns.
Improve SDLC effectiveness through automation, CI/CD, test engineering, observability, secure development practices, and AI-enabled workflows.
Own non-functional requirements and ensure software is production-ready, well-instrumented, compliant, secure, and designed for always-on, high-transaction distributed environments.
Diagnose complex system failures and production issues; lead root cause analysis, remediation, reliability improvements, and technical debt reduction.
Partner with Product, Architecture, Security, SRE, Platform, Quality Engineering, and business stakeholders to translate functional and non-functional needs into implementable technical solutions.
Technical Mentoring and Organizational Influence
Mentor senior engineers, technical leads, and developers in architecture, design principles, design patterns, coding practices, testing, production engineering, and AI-assisted development.
Raise engineering capability through technical forums, design walkthroughs, workshops, communities of practice, and documented engineering standards.
Influence technology choices and engineering direction across teams without relying on direct authority.
Build AI proficiency through coaching on prompt engineering, context engineering, AI-assisted design, AI-powered testing, AI-driven troubleshooting, and effective use of autonomous agents.
Communicate complex technical concepts, options, risks, and recommendations clearly to technical and non-technical audiences.
Innovation and Continuous Improvement
Evaluate emerging technologies and lead Proofs of Concept (PoCs), pilots, and adoption plans based on measurable engineering and business value.
Continuously improve developer experience, engineering tooling, architecture practices, and delivery workflows.
Contribute to enterprise engineering strategy and promote learning, experimentation, knowledge sharing, and evidence-based technical decision-making.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline.
10+ years of professional software engineering experience delivering enterprise-scale solutions, including significant hands-on development and architecture responsibility.
Proven experience providing technical leadership across distributed Agile engineering teams and complex cross-functional initiatives.
Demonstrated success designing and delivering cloud-native, high-volume, fault-tolerant, and globally distributed systems.
Strong hands-on programming experience in Java and related enterprise technologies, including Spring, Spring Boot, J2EE, object-oriented analysis and design, and modern engineering practices.
Deep knowledge of microservices, APIs, event-driven architecture, distributed systems, enterprise integration, REST, messaging, relational and NoSQL databases, and application containerization.
Practical cloud engineering experience; Google Cloud Platform experience is preferred.
Strong knowledge of CI/CD, automated testing, Test-Driven Development, DevSecOps, observability, system monitoring, JVM diagnostics and tuning, networking, and production troubleshooting.
Experience with SaaS, PaaS, IaaS, cloud migration strategies, scripting using Python or Bash, and modern build and deployment tooling.
Strong analytical, system-thinking, problem-solving, communication, and stakeholder-influence skills.
Hands-on experience with AI-assisted software development platforms such as GitHub Copilot, Microsoft Copilot, Cursor, Claude Code, or equivalent technologies.
Strong understanding of Generative AI, LLMs, RAG architectures, agentic workflows, MCP servers, prompt engineering, context engineering, and AI governance.
Experience evaluating, integrating, and scaling AI capabilities within enterprise engineering environments.
Ability to define and apply standards for secure, responsible, and high-quality AI-assisted software development.
Demonstrated ability to connect AI adoption to measurable improvements in engineering productivity, software quality, or developer experience.
Experience in the travel technology, payments, or other high-transaction regulated domains will be a plus.
Experience building AI-enabled developer platforms, internal engineering tools, intelligent automation, or production-grade AI applications.
Experience leading architecture modernization, cloud migration, platform engineering, or operational excellence initiatives.
Demonstrated contribution to technical communities, architecture forums, engineering standards, patents, publications, or open-source initiatives.
We will give careful consideration to your application and review your details against the position criteria. You will receive separate notification as your application progresses.
Please note that only candidates who meet the minimum criteria for the role will proceed in the selection process.
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