The Scrum Master for AI-Enabled Product Delivery is accountable for the end-to-end execution of the Product Development Lifecycle (PDLC) in an workplace 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 workplace 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 Protected 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.

📌 Scrum Master (Mumbai)
🏢 Crisil
📍 Mumbai