Sr Manager, Lead Delivery Leader
- Portfolio-level delivery coordination: Orchestrate execution across multiple product teams, service lines, and enterprise functions. Hold the cross-team view that no single product team can hold for itself.
- Stakeholder orchestration: Hold the relationship with internal Deloitte stakeholders at the program level, service line leaders, function heads, platform owners and reconcile the cross-stakeholder inconsistencies that show up at scale.
- Delivery operating model: Own how Delivery work is governed in your portfolio. Define the templates, intake patterns, evidence gates, and exception paths that allow AI-assisted execution to scale without losing accountability.
- AI-augmented delivery practice: Direct AI agents and assistants in delivery workflows - what they coordinate, what evaluation criteria apply, and where exceptions route to humans.
- Own end-to-end LLM, agentic AI, and cloud consumption costs-governing model selection, orchestration, token usage, and infrastructure efficiency through FinOps practices.
- Drive Applied AI delivery budget planning, forecasting, and tracking aligned to ROI targets, enforcing financial governance and surfacing cost-saving opportunities within defined guardrails.
- Capability stewardship: Coach Delivery Managers in your portfolio. Develop the senior coordination talent the organization needs.
- Strategic Vision and Alignment: Lead large-scale, AI-native implementations and ensure each program delivers the outcome its charter promised, right-sizing program structures to scale and partnering with Product, Engineering, Architecture, and Experience leaders to hold the strategic line.
- Evangelism: Inspire teams and educate the organization on product engineering models, actively experimenting with GenAI so the discovery and delivery practices you advocate stay current.
- Craft Mastery and Objectives Realization: Drive evidence-gated delivery - work advances when deployed evidence confirms intended behavior, not at a sprint boundary - and define the standards, evaluation criteria, and exception handling by which AI agents participate in delivery, using telemetry and audit trails for quality and KPI tracking.
- Capability Evolution and Development: Coach and calibrate Delivery Managers, building their fluency in AI-native discovery and delivery methodologies and growing the Delivery community of practice.
- Operational Excellence: Run alignment cadence at the level the work demands, and embed continuous, evidence-backed governance in the pipeline (policy as code, telemetry, immutable audit trails) rather than periodic gates - carrying programs through operate with durable accountability.
- Customer-Centric Problem Solving: Anchor teams to the charter outcome, treating "deployed evidence confirms intended behavior" as the real definition of done, and carry initiatives into adoption so value lands in the customer's hands.
- Expert Proficiency and Continuous Improvement: Keep the engineering model lean by governing intake of AI-generated artifacts so they become productive inputs rather than noise, defining how AI agents operate in workflows, and using evidence gates to cut work that isn't earning its place.
- Communication: Reconcile conflicting stakeholder and AI-drafted artifacts on the same initiative, run the forums where alignment happens, and escalate deliberately - surfacing trade-offs and holding the room until a decision is made.
- Delivery Risk Management: Manage dependencies across products and platforms, using evidence-gated cycles, continuous governance, and audit trails as the primary risk controls to surface issues early and decide contingencies before they become incidents.
- Organizational Engagement and Collaboration: Engage stakeholders across Product, Experience, and Engineering, govern AI-generated artifact intake as a partnership, and model senior Delivery behaviors: judgment under uncertainty, stakeholder steadiness, and evidence orientation.
The Team US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value and outcomes. As Deloitte's primary product development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom-line results. We develop and deploy cutting-edge go-to-market solutions that help Deloitte operate effectively and lead the market. Our reputation is built on a tradition of delivering excellence. Qualifications Required
- Experience: 15+ years of progressive delivery leadership experience in product engineering, enterprise technology, or management consulting environments.
- Demonstrated track record leading large, cross-functional programs - defined as programs with multiple product teams, multiple stakeholders, and meaningful cross-function dependencies.
- Direct experience with both Agile/Lean operating models and the emerging intent-driven, evidence-gated delivery patterns.
- Experience working in a product-staffed model where durable product teams stay with their products across build, deploy, operate, and evolve.
- Strong expertise in AI/Cloud FinOps, budget management, financial forecasting, vendor management, cost optimization, and data-driven delivery governance.
- Prior experience coaching Delivery Managers or Program Managers.
- Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available.
- Candidates must be located within a commutable distance to one of the select locations available for this role
- Ability to work in your local office at a minimum of 3 days per week
- Engineering fluency: Comfortable in technical conversations with architects and senior engineers; able to challenge feasibility claims and reason about trade-offs without needing translation.
- Stakeholder orchestration at scale: Track record of holding alignment across senior internal stakeholders with competing priorities.
- Evidence orientation: Comfortable measuring delivery by outcome validation rather than velocity proxies.
- AI-augmented working practice: Fluent with current GenAI assistants and agentic delivery tooling. Able to direct AI output and design where AI agents fit in delivery workflows and where humans must remain in the loop.
- Communication: Exhibit excellent communication skills to inspire and influence stakeholders. Serve as the contact for value achievement and collaborate with product engineering teams. Facilitate co-creation workshops to foster alignment and decision-making
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