Scrum Master for AI-Enabled Product Delivery

Scrum Master

Department

None

Job Description

The Scrum Master for AI-Enabled Product Delivery is accountable for the end-to-end execution of the Product Development Lifecycle (PDLC) in an environment where Artificial Intelligence is embedded at every stage. This role is directly aligned to the organisation's 2026 Technology Goals — spanning Quality Engineering & Test Automation, Release & Environment Discipline, and Incident & Reliability Management — and is responsible for ensuring those goals are achieved through disciplined agile delivery, AI-augmented tooling, and unwavering team accountability.

The Scrum Master owns delivery outcomes: on time, on scope, on quality. They are the single accountable leader who bridges agile process, engineering rigour, AI capability, and business trust

  Key Responsibilities

1.  Delivery Ownership & Accountability

▸  Own sprint commitment end-to-end: track burn-down daily, identify deviation early, and drive corrective action before the sprint ends — not after.

▸  Maintain a rolling 3-sprint delivery forecast with confidence scores published to stakeholders at every sprint review.

▸  Own the impediment log with SLAs: P1 blockers ≤24 hours, P2 ≤48 hours, P3 ≤5 days. Escalate rapidly; never let blockers sit.

▸  Ensure Definition of Readiness (DOR) is met for each sprint by working with PO/BA.

▸  Own the Definition of Done — ensure it incorporates the organisation's 2026 standard: peer review, automated test evidence, parity validation, change ticket, and rollback plan.

▸ Protect team capacity from unplanned work; maintain transparently with the Product Owner and stakeholders.

▸  Ensure every production release follows the Release Calendar, carries a change ticket and rollback plan, and has Business sign-off before deployment begins.

2.  Quality Engineering & Definition of Done (FA1)

▸  Enforce the redefined Definition of Done in every sprint — DoD is non-negotiable

▸  Drive PR review compliance: 2-reviewer rule enforced for high-risk changes; PR rejection log reviewed monthly with tech leads.

▸  Track defect leakage rate as a first-class sprint metric. Own fortnightly trend reviews with QA; report monthly to Business.

▸  Ensure no story is released without automated test suite; manual QA only via documented exception with named sign-off.

3.  Release & Environment Discipline (FA2)

▸  Own the team's release calendar adherence: ≥90% of releases within agreed windows; all exceptions documented with named Business approval.

▸  Maintain UAT/QA/Production parity matrix; ensure undocumented environment differences as added as defects in the backlog.

▸  Ensure every production deployment has a change ticket created before work begins, a plain-language release summary, and a tested rollback plan.

4.  Incident Management & Operational Reliability (FA3)

▸  Ensure 100% of production incidents are logged in the system of record with complete mandatory fields — impact, timeline, detection method, and Business-confirmed impact statement.

▸  Enforce RCA SLAs: Sev-1 draft within 5 days / final within 10 days; Sev-2 draft within 10 days / final within 20 days.

▸  Ensure all corrective actions are tracked with named owners and due dates; overdue items escalated to monthly reliability review.

▸  Auto-flag same-root-cause repeat incidents within 60 days; escalate to executive review with a revised corrective action plan.

5.  AI-Augmented Agile Facilitation

▸  Facilitate all Scrum ceremonies (PI Planning ,Sprint Planning, Daily, Review, Retrospective, Refinement) with AI-generated pre-reads, velocity benchmarks, and risk summaries.

▸  Facilitate all Scrum ceremonies (PI Planning ,Sprint Planning, Daily, Review, Retrospective, Refinement) with AI-generated pre-reads, velocity benchmarks, and risk summaries.

▸  Leverage AI retrospective platforms to identify recurring friction patterns across sprints and drive systemic, evidence-based improvement.

▸  Monitor DORA metrics (Deployment Frequency, Change Lead Time, Change Fail Rate, Recovery Time) alongside Flow Metrics (Flow Time, Flow Load, Flow Efficiency) as the team's engineering health indicators.

▸  Govern AI tool adoption responsibly: Ensure human review of all AI-generated artefacts, champion bias awareness and data privacy compliance.

6.  Team Performance, Coaching & Continuous Improvement

▸  Coach team members on agile values — not just process compliance. Embed Scrum's five values (Commitment, Courage, Focus, Openness, Respect) into team culture.

▸  Drive quarter-on-quarter velocity improvement using AI analytics; target ≥5% QoQ improvement in delivery throughput.

▸  Monitor team health and psychological safety via AI-sentiment tools; intervene proactively on burnout and disengagement signals.

 Qualifications & Experience

Essential

▸  5+ years as a Scrum Master or Agile Delivery Lead in a software product environment with direct delivery accountability.

▸  Proven track record of owning end-to-end product releases: on time, on scope, on quality — not just facilitating the process.

▸  Hands-on experience with Definition of Done implementation, PR governance, and quality gate enforcement in ADO or Jira.

▸  Demonstrated experience driving defect leakage reduction, automated testing adoption, and release discipline across teams.

▸  Familiarity with incident management, RCA processes, and operational reliability frameworks (ITIL or equivalent).

▸  Proficiency with delivery platforms: Jira, Azure DevOps, Confluence, or equivalent — including workflow configuration.

▸  Strong data literacy: DORA metrics, Flow Metrics, velocity trends, burn-down analysis, and AI analytics dashboards.

▸  Hands-on experience with AI-assisted delivery tools (e.g., GitHub Copilot, Jira AI, or equivalent).

▸  Excellent stakeholder communication — able to present delivery health in plain language to non-technical Business audiences.

Preferred / Advantageous

▸  CSM, A-CSM, CSP-SM, or SAFe Scrum Master (SSM) certification.

▸  Experience with DORA metrics implementation and DORA Elite performance band targets.

▸  Background in QA, DevOps, or engineering — providing genuine empathy for technical delivery challenges and quality practices.

▸  Familiarity with CI/CD pipeline governance, automated testing frameworks, and DevSecOps practices.

▸  Knowledge of AI governance, responsible AI principles, and digital ethics frameworks.

▸  Exposure to regulated or audit-driven environments where evidence-based delivery is mandatory.

Open Positions

1

Mandatory Skills

Agile Scrum Master, Agile

Education Qualification

Graduate

Experience

5 to 9 years