Sr AI Technical Program Manager

T-Mobile T-Mobile · Telecom · Seattle, WA +2

Sr AI Technical Program Manager responsible for end-to-end execution of T-Mobile's AI product portfolio, managing multiple product launches, cross-functional teams, and enterprise partnerships. Owns program plans, risk management, dependency coordination, and executive reporting for a multi-product AI platform. Emphasizes automation and agentic workflows to optimize program operations and scale delivery.

What you'd actually do

  1. Owns the Network AI master program plan across all active products PODs Defines and manages cross-product milestones, dependency maps, and critical path analysis. Runs weekly program steering and bi-weekly executive steering with senior leadership. Coordinates delivery across Network, IT/AI Platform, AI COE, and Business Teams. Ensures every POD has clear scope, timeline, and acceptance criteria. Leads the interface between internal delivery teams and external partners. Work with the GTM team on certification readiness process and launch sequencing across simultaneous products.
  2. Identifies and eliminates tactical program, project scrum management overhead through automation. Builds and maintains automated dashboards for program health, epic status, risk tracking, and budget reporting - replacing manual status collection with real-time data. Implements agentic workflows for dependency tracking, milestone alerts, and stakeholder notifications. Designs repeatable program templates (launch checklists, readiness scorecards, war room protocols) that scale across product launches without linear headcount growth. Establishes automated executive reporting pipelines that pull directly from Jira/Asana/project management tools rather than requiring manual synthesis. The goal is to ensure the program function operates at scale through tooling and process design, not through adding coordinators.
  3. Leads the program risk register across Capex/Opex portfolio. Identifies, assesses, and drives mitigation for delivery risks spanning technical dependencies (platform capabilities gating product features), vendor dependencies (Vendor SOW deliverables, model provider timelines), regulatory dependencies, and commercial dependencies.
  4. Serves as the single source of truth for program status across all stakeholders. Prepares and delivers executive-ready program reviews that lead with decisions needed, not status recaps. Manages communication cadence across different audiences: weekly tactical syncs with POD leads, bi-weekly steering with senior leadership, monthly business reviews with cross-functional partners. Translates technical delivery status into business impact language for non-technical executives. Coordinates with IT PMO, Network PMO, and enterprise governance processes to ensure program reporting integrates with T-Mobile's broader portfolio management.
  5. Designs the operating rhythm for a multi-product AI program: sprint cadences, release trains, launch readiness gates, and retrospective cycles. Implements process controls for vendor management (SOW tracking, deliverable acceptance, co-investment reporting) and regulatory compliance. Establishes the war room protocol for launch events and critical escalations. Drives post-launch retrospectives that produce actionable improvements, not just documentation. Builds the program management playbook that enables the team to scale from current products to the full 2027-2028 roadmap.

Skills

Required

  • Program Management
  • Technical Program Management
  • AI/ML development cycles
  • Cloud platform architectures
  • Carrier-grade network integration
  • Risk Management
  • Dependency Coordination
  • Executive Reporting
  • Stakeholder Communication
  • Process Design
  • Automation Tooling
  • Agentic Workflows

Nice to have

  • Jira
  • Asana

What the JD emphasized

  • automation-first mindset
  • agentic workflows
  • scale multiple active products
  • on-time delivery
  • stakeholder confidence

Other signals

  • AI product portfolio execution
  • cross-functional PODs
  • enterprise partnerships
  • network AI product launches
  • multi-product AI platform program
  • automation-first mindset
  • AI/ML development cycles
  • cloud platform architectures
  • carrier-grade network integration
  • on-time delivery
  • stakeholder confidence
  • scale multiple active products