Facilitate all core agile ceremonies (stand-ups, sprint planning, reviews, retrospectives)
Ensure teams are operating effectively within agile frameworks (Scrum/Kanban), driving continuous improvement
Support the transition from AI use case discovery and experimentation into structured, production delivery
Support delivery of AI initiatives, including LLM-based capabilities, agent workflows, and data-driven features
Help teams manage the lifecycle of AI products (experimentation → evaluation → productionisation → scaling)
Ensure alignment between platform development, data dependencies, and platform engineering deliverables
Coordinate activities such as testing, capability iteration, evaluation metrics, and release readiness
Manage dependencies across platform, data, security, and AI engineering teams
Align multiple squads contributing to the AI platform to shared milestones and outcomes
Enable collaboration between technical and non-technical stakeholders (e.g. Product, Governance, Security)
Proactively identify and remove impediments, particularly across data access, environment readiness, and governance processes
Drive focus on prioritised outcomes, reducing inefficiencies and delivery friction
Improve team velocity, predictability, and flow across iterative AI development cycles
Act as a bridge between engineering teams and leadership, providing clear updates on delivery progress, risks, and dependencies
Support governance processes (architecture, security, responsible AI) while maintaining delivery momentum
Ensure alignment to business outcomes and measurable value
Work closely with Product Owners and Architecture to maintain a prioritised, well-defined backlog in Jira
Ensure user stories (including AI-related work such as model integration, data pipelines, and evaluation criteria) are clear, actionable, and appropriately sized
Support roadmap planning, sprint forecasting, and incremental delivery of platform capabilities
Own and maintain delivery tracking within Jira (boards, workflows, reporting)
Utilise planning and collaboration tools (e.g. Confluence, Miro, roadmap tools) to support transparency and alignment
Track and report on key delivery metrics (velocity, throughput, cycle time, predictability, AI experiment success rates where applicable)
Provide concise updates for leadership and steering forums
Requirements
Proven experience as a Scrum Master in complex, cross-functional engineering environments (platform, data, or AI-focused teams preferred)
Strong understanding of agile methodologies (Scrum, Kanban, scaled delivery models)
Experience supporting delivery of AI/ML or data-driven products (e.g. LLMs, automation, analytics platforms)
Proficiency with Jira for backlog management, sprint planning, and reporting
Familiarity with planning and collaboration tools (e.g. Confluence, Miro, roadmapping tools)
Understanding of cloud platforms (preferably AWS) and modern software delivery practices
Ability to manage multiple stakeholders and navigate organisational complexity
Strong facilitation, communication, and problem-solving skills
Experience in governance-heavy or regulated environments is advantageous