AI Platform Delivery Lead

EY; Jacksonville (FL); Hybrid; USD 180,000 - 320,000; Full time

Job summary

EY is seeking a seasoned Technical Delivery Manager to drive day-to-day execution of engineering teams building EY’s AI platform. You will own delivery cadence, run scrums, and ensure work flows while challenging estimates and scope with evidence.

You will work with senior engineers to interrogate dependencies, manage cross-team deliverables, and report progress with honest metrics. This role requires deep technical fluency and a track record of on-time delivery.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or demonstrably equivalent depth.
• 7+ years in technical delivery, engineering project or program management, or Scrum Master roles supporting software engineering teams, with direct accountability for delivery outcomes.
• Hands-on experience as a software or platform engineer, or demonstrably equivalent technical depth.
• Ability to evaluate engineering estimates, work decomposition, and technical risk independently, and to challenge them constructively with evidence.
• Proven track record running Scrum or Kanban for teams of experienced engineers, owning the full set of ceremonies and the health of the backlog.
• Experience delivering complex, multi-team technical programs with hard cross-team dependencies and phased delivery gates.
• Working fluency with modern cloud-native engineering: Kubernetes and containers, CI/CD, infrastructure-as-code, distributed systems, and API design.
• Experience with engineering delivery tooling (Jira or equivalent) and Git-based workflows, including the delivery metrics they produce.
• Evidence of holding delivery teams to timelines and quality standards while retaining their trust and engagement.
• Ability to communicate delivery status, risk, and trade-offs to senior stakeholders with clarity and candour.

Responsibilities

• Own the delivery cadence: run daily stand-ups, sprint planning, backlog refinement, sprint reviews, and retrospectives across the engineering teams, keeping every ceremony short, decision-focused, and genuinely useful to the engineers in the room.
• Drive progress against commitments: track every in-flight item through to completion, surface slippage on the day it happens rather than at sprint end, and ensure blocked work is actually unblocked rather than quietly rescheduled.
• Hold engineers accountable to deliverables and timelines: agree explicit definitions of done and dated commitments, follow them up personally, and elevate early and credibly when delivery is at risk.
• Interrogate estimates and challenge padding: apply your own technical judgement to test sizing, decomposition, and complexity claims, separate genuine engineering risk from conservative buffering, and hold estimates to what the work actually requires.
• Break down ambiguous work: translate architecture intent, phase gates, and platform capability requirements into properly sized, independently deliverable stories with unambiguous acceptance criteria.
• Own sprint and release planning across the phased platform build: sequence work so each phase puts usable capability into engineering teams’ hands, and defend entry and exit gates rather than letting them soften under schedule pressure.
• Manage cross-discipline dependencies: coordinate hand-offs between platform and infrastructure, runtime and observability, data and state, trust infrastructure, security, and the AFE delivery cells.
• Own delivery risk and issue management: maintain a live view of risks, dependencies, and the critical path, drive mitigation to closure, and make trade-offs explicit to leadership while there is still time to act on them.
• Report delivery status with evidence: velocity, throughput, cycle time, flow efficiency, escaped defects, and burn against phase gates, reported honestly and without softening, including when the news is bad.
• Protect the team’s focus: absorb interruptions, scope injection, and competing stakeholder demands so engineers get uninterrupted delivery time.
• Partner with engineering leads and architects: on sequencing, staffing, and technical direction, with a clear boundary that they own the technical decision and you own whether the work lands on time and to standard.

Skills

Technical depth

Agile practice

Credibility with engineers

Estimate evaluation

Scrum & Kanban

Cloud-native

Strong communication

Education

Bachelor’s or Master’s degree in Computer Science or Engineering

Tools

Jira

Git

Job description

EY is seeking a seasoned Technical Delivery Manager to drive day-to-day execution of engineering teams building EY’s AI platform. You will own delivery cadence, run scrums, and ensure work flows while challenging estimates and scope with evidence.

You will work with senior engineers to interrogate dependencies, manage cross-team deliverables, and report progress with honest metrics. This role requires deep technical fluency and a track record of on-time delivery.