Sr Manager Software Engineering

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.

The Sr Engineering Manager is a transformational technology leader responsible for building high-performing engineering teams while driving enterprise-wide adoption of AI-native software engineering practices. This role combines people leadership, technical excellence, delivery accountability, architectural governance, and AI transformation leadership to improve engineering productivity, software quality, developer experience, and business outcomes.

The ideal candidate is a visionary engineering leader who views AI as a foundational engineering capability rather than simply a productivity tool. They possess the ability to influence engineering culture, drive behavioural change through coaching and metrics, and build high-performing teams that embrace continuous improvement. They balance innovation with governance, quality, security, and compliance, while creating an environment where engineers can leverage AI to deliver better software, faster and more effectively.

The successful candidate will establish AI as a core engineering capability across the Software Development Lifecycle (SDLC), leveraging AI-assisted development, autonomous agents, engineering copilots, and modern developer platforms to accelerate delivery while maintaining the highest standards of security, quality, reliability, and compliance.

Responsibilities

AI Engineering Transformation

• Define and execute the AI engineering adoption strategy across software development teams.

• Establish AI-first engineering practices that integrate AI throughout requirements analysis, design, coding, testing, documentation, deployment, operations, and support.

• Drive widespread adoption of approved AI coding assistants, engineering copilots, autonomous agents, and AI-enabled developer platforms.

• Identify opportunities to automate repetitive engineering, testing, documentation, operational, and support activities using AI.

• Lead organizational change initiatives that transition teams from traditional software development models to AI-augmented engineering practices.

AI Governance, Standards & Engineering Excellence

• Establish governance frameworks for responsible, secure, and compliant use of AI-generated code and engineering artifacts.

• Define standards, best practices, quality controls, validation processes, security reviews, and intellectual property safeguards for AI-assisted development.

• Create reusable engineering playbooks, implementation patterns, and AI adoption guidelines.

• Ensure adherence to enterprise AI governance, risk management, security, and compliance requirements.

• Balance innovation and rapid adoption with engineering discipline and operational excellence.

Engineering Leadership & People Management

• Lead, mentor, coach, and develop software engineers, technical leads, and senior engineering talent.

• Build a culture of innovation, accountability, continuous learning, experimentation, psychological safety, and technical excellence.

• Drive workforce planning, hiring, onboarding, succession planning, performance management, career development, and talent retention.

• Establish AI proficiency as a core engineering competency across teams.

• Coach engineers on prompt engineering, AI-assisted design, AI-powered testing, AI-driven troubleshooting, and effective use of autonomous agents.

• Build internal AI champions and communities of practice to accelerate organizational capability development.

Technical Leadership & Architecture

• Provide hands-on technical leadership through architecture reviews, design reviews, code reviews, and technology evaluations.

• Guide teams in integrating Generative AI services, LLM platforms, RAG architectures, agentic workflows, MCP servers, and emerging AI technologies.

• Promote AI-assisted architecture analysis, code generation, remediation, testing, documentation, and operational support practices.

• Ensure engineering solutions meet enterprise standards for scalability, performance, reliability, maintainability, observability, and security.

• Mentor teams in software architecture, distributed systems, cloud-native engineering, object-oriented design, and modern engineering practices.

Delivery Excellence & Operational Leadership

• Own the successful delivery of complex software initiatives across multiple teams.

• Ensure AI capabilities are effectively leveraged to improve delivery velocity without compromising quality.

• Drive continuous improvement of SDLC processes through automation, AI-enabled workflows, and engineering best practices.

• Reduce cycle times, increase deployment frequency, improve throughput, and enhance predictability of delivery.

• Manage technical risks, dependencies, stakeholder expectations, and cross-functional alignment.

• Lead operational excellence initiatives including production diagnostics, incident management, root cause analysis, reliability improvements, and technical debt reduction.

Capability Building & Innovation

• Design and execute AI capability development programmes for engineers at all levels.

• Lead workshops, training sessions, architecture forums, and innovation programmes focused on AI-assisted engineering.

• Sponsor Proof of Concepts (PoCs), pilots, and experimentation initiatives to evaluate emerging AI technologies.

• Partner with platform, architecture, product, security, and engineering teams to evolve AI-enabled developer experiences and engineering tooling.

• Continuously evaluate industry trends and identify opportunities to improve engineering effectiveness through AI.

Success Metrics

AI Adoption

• Percentage of engineers actively using approved AI tools.

• Adoption of AI-assisted coding, testing, documentation, and design practices.

• Utilisation of engineering copilots, agents, and AI-enabled workflows.

Engineering Productivity

• Reduction in development cycle times.

• Improvement in sprint velocity, throughput, and deployment frequency.

• Reduction in manual and repetitive engineering activities.

• Increased engineering efficiency and developer effectiveness.

Quality & Governance

• Improvement in code quality and maintainability metrics.

• Reduction in escaped defects and technical debt.

• Security, compliance, and governance adherence for AI-generated artifacts.

• Increased automation coverage across development and testing.

People & Culture

• Team engagement, retention, and career progression.

• Growth in AI engineering capability and technical maturity.

• Completion of AI enablement programmes and certifications.

• Expansion and effectiveness of internal AI communities of practice.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline.

• 10+ years of software engineering experience delivering enterprise-scale software solutions.

• 5+ years of engineering leadership experience with direct people management responsibilities.

• Proven experience leading distributed Agile engineering teams.

• Demonstrated success delivering complex cloud-native and distributed systems.

Technical & AI Expertise

• 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, and AI governance.

• Experience evaluating, implementing, and scaling AI technologies within engineering organisations.

• Ability to define engineering standards and governance models for AI-assisted development.

• Demonstrated capability to measure and deliver productivity improvements through AI adoption.

Core Technical Competencies

• Strong software engineering background in Java, Python, microservices, APIs, distributed systems, and cloud-native architectures.

• Experience with Google Cloud platforms.

• Deep understanding of CI/CD, automated testing, DevOps, observability, operational excellence, and modern software delivery practices.

• Strong architectural design, system thinking, and technical decision-making capabilities.

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.