Sr Manager, Lead Delivery Leader

This role is for a Sr. Manager, Lead Delivery Leader within Deloitte's Technology Product Engineering team, focusing on AI-native implementations. The individual will be responsible for portfolio-level delivery coordination, stakeholder orchestration, and defining the delivery operating model for AI-augmented practices. Key responsibilities include directing AI agents in delivery workflows, managing LLM and agentic AI costs, driving AI delivery budget planning, and coaching Delivery Managers in AI-native methodologies. The role emphasizes evidence-gated delivery and continuous governance, ensuring AI agents operate effectively within defined guardrails and contribute to ROI targets.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Own end-to-end LLM, agentic AI, and cloud consumption costs—governing model selection, orchestration, token usage, and infrastructure efficiency through FinOps practices.

Skills

Required

  • 15+ years of progressive delivery leadership experience in product engineering, enterprise technology, or management consulting
  • Experience leading large-scale, AI-native implementations
  • Experience with AI-augmented delivery practices
  • Experience managing LLM and agentic AI costs
  • Experience with FinOps practices
  • Experience with AI delivery budget planning, forecasting, and tracking
  • Experience with AI guardrails
  • Experience coaching Delivery Managers in AI-native methodologies
  • Experience with evidence-gated delivery
  • Experience with continuous governance and audit trails

Nice to have

  • Experience with GenAI experimentation
  • Experience with AI agent coordination and evaluation criteria
  • Experience with AI-assisted execution scaling
  • Experience with stakeholder alignment at enterprise scale
  • Experience with dependency management on programs spanning multiple products and functions

What the JD emphasized

  • AI-native implementations
  • AI-augmented delivery practice
  • AI agents
  • agentic AI
  • evidence-gated delivery
  • AI agents participate in delivery
  • AI-native discovery and delivery methodologies
  • AI-generated artifacts
  • AI agents operate in workflows
  • AI-drafted artifacts
  • AI-generated artifact intake

Other signals

  • AI-augmented delivery practice
  • Own end-to-end LLM, agentic AI, and cloud consumption costs
  • Drive Applied AI delivery budget planning
  • Lead large-scale, AI-native implementations
  • actively experimenting with GenAI
  • Drive evidence-gated delivery
  • define the standards, evaluation criteria, and exception handling by which AI agents participate in delivery
  • Coach and calibrate Delivery Managers, building their fluency in AI-native discovery and delivery methodologies
  • govern intake of AI-generated artifacts
  • defining how AI agents operate in workflows
  • using evidence gates to cut work that isn't earning its place
  • Reconcile conflicting stakeholder and AI-drafted artifacts
  • govern AI-generated artifact intake as a partnership