Delivery Lead

Ben Fatto

About The Position

The Kanban / Agentic AI Task Master is accountable for managing the end-to-end flow of engineering work—from intake to delivery—while leveraging agentic AI tooling to increase throughput, reduce friction, and surface blockers before they turn into delays.

Requirements

7+ years in engineering delivery roles (Scrum Master, Delivery Lead, Engineering Manager, or equivalent).
Deep fluency in Kanban and flow metrics ( cycle time, lead time, CFDs ).
Hands-on experience with agentic AI tools, LLM-based automation, or prompt engineering in a delivery/scrum context—with a proven track record of driving team-wide adoption, not just personal use.
Strong proficiency with delivery/project tooling— Azure DevOps (ADO).
Demonstrated ability to lead without direct authority and influence cross-functional teams (including Tech Leads and QA), ensuring accountability is shared—not absorbed.
Direct, proactive communicator who escalates early, surfaces risks without spin, and informs leadership before issues become surprises—always with a mitigation plan in motion.
Strong ownership mindset across time zones/off-hours; does not allow a Friday blocker on a Monday deadline to go unaddressed over the weekend.
Treats Definition of Done as a true gate—enforces pre-delivery verification rather than discovering gaps in production or during war rooms.
Comfortable operating at the intersection of process discipline and people coaching —knows when to tighten the system and when to develop the person.

Responsibilities

Own and continuously optimize the Kanban system across workstreams: backlog health, WIP limits, queue management, cycle time, and throughput.
Proactively identify and remove blockers; escalate impediments with urgency and drive resolution.
Maintain a healthy, prioritized backlog in partnership with Product and key stakeholders.
Drive on-time delivery against committed dates with zero tolerance for silent slippage.
Define and enforce Definition of Ready (DoR) and Definition of Done (DoD) standards.
Ensure consistent team usage of agentic AI workflows that automate task tracking, status updates, sprint hygiene, and reporting.
Leverage agentic tooling to manage and communicate the Azure DevOps (ADO) backlog and delivery schedule to internal and external stakeholders.
Identify high-friction manual processes and build agent-driven solutions to replace them.
Deliver measurable automation outcomes (e.g., reduced time spent on reporting and follow-ups).
Hold the team accountable to delivery commitments— dates set by the team are firm.
Manage individual performance through clear objectives, regular 1:1s, and ongoing feedback cycles.
Partner with Engineering leadership on headcount planning, hiring, and team structure.
Remove organizational obstacles that negatively impact team velocity and quality.
Own team-level OKRs and report progress transparently to leadership.
Facilitate daily standups, planning sessions, and retrospectives with active engagement—ensuring each team member leaves with next steps, expected outcomes, and blockers clearly stated (not passive status reporting).
Coach team members on Kanban principles, flow efficiency, and continuous improvement.
Shield the team from unplanned interruptions while maintaining stakeholder alignment.
Challenge the team to maintain the quality, delivery dates, and velocity they committed to.
Require active participation from all team members in sprint/flow activities.
Champion process improvements raised in retros and drive them through completion.