Delivery Leader, Core Financial Systems

This role is for a Delivery Leader responsible for overseeing multiple product portfolios within core financial systems at Deloitte. The leader will set delivery strategy, manage enterprise programs, coach other leaders, and govern AI workflows, LLM/agentic AI costs, and applied AI budgets. The role emphasizes strategic vision, evangelism of AI-native practices, and capability evolution in an AI-native SDLC.

What you'd actually do

  1. Cross-portfolio delivery strategy: Set delivery strategy for your service lines and contribute to capability-wide strategy with the Capability Lead.
  2. Enterprise program leadership: Lead the most consequential enterprise programs — multi-year, multi-service-line, multi-capability transformations — with accountability equivalent to a Principal individual contributor.
  3. Capability evolution: Partner with the other Capability Leads and serve as the Delivery capability's senior voice with service-line leadership, function heads, and enterprise executives.
  4. Senior coaching: Coach L6 Delivery Leaders and Delivery Managers. Develop the judgment, stakeholder trust, and craft growth they need to progress.
  5. AI-workflow governance at portfolio scale: Define how AI agents participate in delivery across portfolios. Govern evaluation criteria, exception handling, and human-in-the-loop policy at scale.

Skills

Required

  • Program management
  • Project leadership
  • Cross-functional team leadership
  • Enterprise IT solutions
  • Operational excellence
  • Customer-centric problem-solving
  • Innovation
  • Continuous improvement
  • Strategic alignment
  • AI-native practices
  • LLM cost management
  • Agentic AI cost management
  • FinOps practices
  • Applied AI budget planning
  • Forecasting
  • Financial governance
  • Guardrails implementation
  • Delivery strategy
  • Enterprise program leadership
  • Capability evolution
  • Senior coaching
  • AI-workflow governance
  • Evaluation criteria definition
  • Exception handling
  • Human-in-the-loop policy
  • ROI target alignment
  • Cost-saving identification
  • Translating AI/industry signals
  • Orchestration architecture governance
  • AI-assisted execution scaling
  • Evidence-gated cycles
  • Objective/KPI tracking
  • Quality audits
  • GenAI experimentation
  • AI-assisted delivery experimentation
  • AI-native SDLC judgment

Nice to have

  • Financial systems expertise

What the JD emphasized

  • AI-workflow governance at portfolio scale
  • Own end-to-end LLM, agentic AI, and cloud consumption costs
  • Drive Applied AI delivery budget planning, forecasting, and tracking
  • governing the orchestration architecture that lets AI-assisted execution scale
  • evolving that vision by translating external AI/industry signals into capability direction
  • govern the orchestration architecture that lets AI-assisted execution scale without losing accountability
  • define product engineering maturity and advance it through experimentation with GenAI and AI-assisted delivery
  • producing feedback that builds judgment for an AI-native SDLC

Other signals

  • AI-workflow governance at portfolio scale
  • Own end-to-end LLM, agentic AI, and cloud consumption costs
  • Drive Applied AI delivery budget planning, forecasting, and tracking
  • governing the orchestration architecture that lets AI-assisted execution scale