Role Summary

Experienced Scrum Master who will serve as the day-to-day project manager for our AI and digital innovation engineering team. This goes well beyond traditional ceremony facilitation — you will own the team’s operational rhythm, manage scheduling and calendars, break down large initiatives into actionable work items, and ensure the right people are in the right meetings at the right time.

You will be embedded with teams building LLM-powered applications, agentic AI systems, automation platforms, and data infrastructure. You must be comfortable operating in a fast-moving, experimentally-driven environment where sprint goals may involve model evaluation, prompt engineering iterations, and infrastructure provisioning alongside traditional feature development.

Key Responsibilities

Scheduling, Coordination & Team Operations

• Own the team’s calendar: schedule all meetings, workshops, reviews, and working sessions — ensuring efficient use of everyone’s time;
• Coordinate across squads, the Technical Delivery Lead, and the engagement owner to align priorities and resolve scheduling conflicts;
• Manage meeting logistics for distributed/hybrid teams across time zones, ensuring inclusive participation;
• Maintain a clear, up-to-date view of who is working on what, team capacity, and upcoming availability (PTO, on-call, etc.);
• Act as the operational hub — the first person the team goes to for “where, when, and who” questions.

Agile Facilitation & Sprint Ceremonies

• Facilitate all Scrum ceremonies: Sprint Planning, Daily Standups, Sprint Reviews, Sprint Retrospectives, and Backlog Refinement sessions;
• Adapt Agile practices for AI/ML development workflows where work is inherently exploratory (research spikes, model experimentation, data pipeline iteration);
• Establish and maintain sprint cadences (typically 2-week sprints) tailored to the team’s delivery context;
• Facilitate cross-team planning and synchronization for multi-squad delivery (Scrum-of-Scrums, PI Planning where applicable);
• Coach the team on story writing, estimation techniques (story points, t-shirt sizing), and definition of done (DoD) / definition of ready (DoR).

Velocity & Metrics Management

• Track and report team velocity, sprint burndown/burnup, cycle time, lead time, and throughput;
• Analyze velocity trends and capacity data to improve sprint forecasting accuracy;
• Build and maintain Agile dashboards (Jira, Azure DevOps, or Linear) providing real-time visibility into team progress;
• Use data-driven insights to identify bottlenecks, patterns of over-commitment, and areas for process optimization;
• Report delivery metrics and sprint health to the Technical Delivery Lead and stakeholders regularly.

Task Breakdown & Backlog Management

• Break down large initiatives, epics, and roadmap items into well-defined, right-sized user stories and tasks that the team can pull with clarity;
• Work with the Technical Delivery Lead to decompose technical requirements into sprint-compatible increments;
• Maintain a healthy, prioritized, and groomed backlog — ensuring every item has clear acceptance criteria and is estimated before entering a sprint;
• Identify and flag dependencies between tasks, squads, and external teams early in the planning cycle;
• Ensure backlog items trace back to delivery goals and that nothing stalls without a clear owner.

Impediment Removal & Team Enablement

• Proactively identify, track, and resolve impediments blocking team progress — technical, organizational, or process-related;
• Escalate blockers to the Technical Delivery Lead or engagement owner when resolution requires organizational intervention;
• Shield the team from external distractions, scope creep, and unplanned work during active sprints;
• Facilitate dependency management across engineering squads (AI, data, DevOps, automation);
• Coordinate with external teams, vendors, and stakeholders to unblock cross-functional dependencies.

Continuous Improvement & Agile Coaching

• Drive a culture of continuous improvement through effective retrospectives with actionable outcomes;
• Coach team members, Product Owners, and stakeholders on Agile principles, Scrum framework, and Lean thinking;
• Introduce and experiment with complementary practices: Kanban flow visualization, WIP limits, cumulative flow diagrams, and probabilistic forecasting (Monte Carlo);
• Identify and address team health indicators: morale, psychological safety, collaboration patterns, and knowledge silos;
• Champion Agile maturity growth across the organization, sharing best practices and lessons learned.

AI/ML-Specific Agile Adaptations

• Collaborate with AI engineers to structure experiment-driven work into sprint-compatible increments;
• Help the team manage the inherent uncertainty of AI/ML development — balancing research exploration with committed delivery;
• Support the team in defining acceptance criteria for AI-specific deliverables: model accuracy thresholds, latency SLAs, evaluation benchmarks;
• Facilitate data readiness and pipeline dependency tracking as first-class sprint concerns;
• Integrate MLOps and model lifecycle stages into the team's Agile workflow.