AI Efficiency Lead

Job Title: AI Efficiency Lead
- AI Enablement, Dev & QA, Digital Services

Today, adoption of Artificial Intelligence (AI) capabilities relies largely on part‑time champions who support initiatives alongside their primary responsibilities. To help accelerate adoption and improve productivity, we are creating a dedicated role that partners with teams to evaluate how they work and identify opportunities where AI can enhance delivery outcomes across Scrum roles through consistent tools, guidance, and measurable impact. The scope of the role is at the Service Train level and operates across teams and functions within the train. The role also serves as a permanent connection between delivery teams and central transformation teams. By helping teams apply centrally developed AI assets in practical delivery environments, the role supports adoption, helps address implementation challenges, and provides structured feedback to improve future solutions. Working closely with teams, the role enables the transition from awareness and experimentation to sustainable adoption and autonomy. The feedback loop includes identifying effective practices, understanding adoption challenges, sharing practical solutions, and collaborating with central teams to enhance AI assets while maintaining central accountability for quality, consistency, and strategic direction.

Key Accountabilities

AI Adoption Roadmap and Tooling Lead and coordinate the AI adoption roadmap across Development (Dev) and Quality Assurance (QA) activities within the Service Train. Evaluate, select, and help standardize AI tools, including coding assistants, test generation, code review, and documentation solutions, while establishing usage guidelines. Implement, integrate, and scale AI-enabled tools, including agents, copilots, automation capabilities, and knowledge-enabled workflows within delivery environments.

Hands-On Enablement and AI Workflow Adoption Support Coach and support teams in integrating AI into their daily engineering and testing practices through practical guidance, enablement sessions, and community‑building activities. Partner with teams to apply AI-enabled assets in day‑to‑day work and support their progression toward greater confidence, adoption, and autonomy. Collaborate with the Principal QA and Train Architect to align AI practices with quality and architectural standards.

Identify and Address Adoption Challenges Identify factors that may affect adoption, including tooling limitations, usability concerns, access constraints, unclear responsibilities, governance questions, skill development needs, dependencies, and organizational change considerations. Assess whether challenges are local or systemic, identify practical solutions, and collaborate with the appropriate transformation teams when broader action is required.

Feedback and Continuous Improvement Collect and share structured feedback from teams, including recurring themes, adoption challenges, skill development needs, role clarity opportunities, and examples from delivery environments. Partner with central teams to improve AI-enabled assets and ensure they remain practical, relevant, and effective for delivery teams while supporting overall strategic direction.

Impact and Responsible Use Measure and communicate productivity, efficiency, and quality outcomes associated with AI-assisted practices, and continuously refine approaches based on results. Promote responsible, secure, and compliant use of AI in alignment with Amadeus policies, including security, Intellectual Property (IP), and data protection requirements.

Collaboration The role works closely with other Services AI Efficiency Leads, central transformation teams and leaders, delivery teams, and additional transformation stakeholders. Within the Service Train, the role primarily partners with the Principal QA and the Train Architect to support alignment, adoption, and continuous improvement initiatives.

Ideal Candidate

Demonstrated experience in software engineering, development, and/or QA, including practical application of AI and Generative Artificial Intelligence (GenAI) tools across the software development lifecycle. Understanding of the AI tooling ecosystem, including agents, copilots, automation capabilities, AI-assisted delivery solutions, and knowledge-enabled workflows. Technical understanding of engineering practices, platform considerations, and tooling integration, with the ability to collaborate on the development and improvement of AI-enabled assets. Interest in advancing developer productivity, tooling effectiveness, and continuous improvement, with the ability to coach, influence, and support change across geographically distributed teams. Awareness of AI governance, security, and IP considerations. Experience with Agile methodologies, the Scaled Agile Framework (SAFe), and web or digital delivery environments is beneficial.

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