Sr. Director, Agentic Ai, Automation & Fde

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

This role leads the Forward Deployment Engineering (FDE) practice to embed AI engineers within business units to implement agentic AI systems across various corporate functions. The focus is on driving business outcomes, managing AI vendor relationships, and scaling the FDE practice.

What you'd actually do

  1. Build and lead the Forward Deployment Engineering practice: design the FDE operating model, engagement methodology, domain rotation structure, and performance measurement framework. Own FDE hiring, onboarding, and cohort development for the initial 20-person US and India cohort and subsequent hiring waves. Establish the practice standards, diagnostic frameworks, and iteration protocols that govern how FDEs operate across all T-Mobile business domains.
  2. Own the Agentic AI Automation delivery function: oversee the design, deployment, and production optimization of agentic AI systems across T-Mobile's commercial domains care, retail, B2B, supply chain, finance, legal, and HR. Ensure deployed solutions are built on the CoE shared platform rather than bespoke per-BU stacks and hold accountability for whether business unit outcome metrics actually move.
  3. Manage the BU partnership model and AI vendor relationships: establish and maintain Sr. Director level relationships with back-office and consumer business unit leaders across all FDE-embedded domains. Own the domain prioritization framework with primary focus on back-office functions. Manage T-Mobile’s strategic partnerships with Google, Anthropic, and OpenAI including joint roadmap input, early access to emerging agentic capabilities, and translating partner technology advances into T-Mobile deployments. Translate BU operational friction into structured feedback for CoE engineering and governance teams.
  4. Drive the reusable pattern library and CoE knowledge loop: ensure proven domain solutions are codified into templates, building blocks, and playbooks that land on the CoE shared platform. Oversee structured bi-weekly feedback cycles from FDEs to CoE engineering teams and participate in CoE sprint reviews to translate field observations into platform improvements.
  5. Report program performance and EBITDA impact: own the roll-up reporting of FDE domain outcomes to SVP and ELT audiences. Build and maintain the measurement framework for tracking AI-driven business impact across domains, including AHT reduction, containment rates, conversion lift, procurement cycle compression, and other BU-specific metrics.

Skills

Required

  • Bachelor's Computer Science, Engineering, or a related field
  • 10+ years of experience in technology leadership with progressive experience in AI engineering applied ML, or enterprise digital transformation
  • 7-10+ years of experience in people leadership managing technical teams including senior engineers and team leads
  • 2-4 years hands-on experience with Agentic AI systems, LLM deployment, RAG architecture, or multi-agent orchestration in production environments
  • Deep understanding of Agentic AI system design, prompt orchestration, LLM integration patterns, and production AI reliability
  • Ability to define, instrument, and report on business outcome metrics tied to AI deployment not activity or deployment milestones
  • Demonstrated ability to lead technical delivery in ambiguous, operational environments without formal authority
  • Ability to translate AI field performance and domain outcome data for SVP and ELT audiences
  • Track record of building and scaling a new technical practice, center of excellence, or field engineering function
  • U.S. citizenship

Nice to have

  • Familiarity with operations in at least two of the target domains: care, retail, B2B, supply chain, finance, HR

What the JD emphasized

  • Agentic AI
  • Forward Deployment Engineering
  • scale
  • business unit outcome metrics
  • production optimization
  • agentic AI systems
  • strategic partnerships with Google, Anthropic, and OpenAI

Other signals

  • embedding specialized AI engineers directly inside business units
  • scale
  • agentic AI systems
  • production optimization
  • business unit outcome metrics
  • strategic partnerships with Google, Anthropic, and OpenAI