Principal Forward Deployment Engineer

T-Mobile T-Mobile · Telecom · Bellevue, WA +1

Principal Forward Deployment Engineer responsible for setting the technical direction and building shared components for agentic AI delivery across T-Mobile's FDE practice. The role requires hands-on production code and shaping broader AI direction, with performance measured on validated ROI. Focus is on enabling faster, more reliable, and scalable agentic AI solutions.

What you'd actually do

  1. Set the technical direction for agentic AI delivery across the practice, keeping the team well positioned as the model and tooling landscape evolves.
  2. Establish the architectural approaches the practice builds on, and shape which problems are worth solving with agentic AI.
  3. Drive delivery consistency across domains so quality holds regardless of who is deployed. Step in where engagements need senior technical help.
  4. Build and maintain the shared technical foundations the rest of the team delivers on. Generalize what works in one domain so others can move faster.
  5. Take on the most complex technical challenges the practice encounters, staying hands-on while keeping cost, performance, and reliability in view

Skills

Required

  • Production Python Engineering
  • LLM System Architecture
  • Data Engineering & Integration
  • Cloud-Native Deployment & MLOps
  • Organizational Technical Leadership
  • Technical & Portfolio Judgment
  • Data Security & Privacy

Nice to have

  • Go
  • TypeScript
  • Java
  • AWS
  • Docker
  • Kubernetes
  • CI/CD
  • vector databases
  • observability
  • Telecom Domain Experience
  • Applied Machine Learning & Data Science
  • Conversational AI, Intelligent Automation, or RPA
  • Regulated Data Environments (CPNI, PII, PCI, or HIPAA)
  • Hiring & Technical Assessment Contribution
  • Depth across NLP, embeddings, transformer architectures, and foundation models

What the JD emphasized

  • 3+ years deploying LLM-based systems in production
  • 2+ years delivering agentic or multi-agent systems
  • Track record of AI solutions that measurably improved a business outcome
  • Production Python Engineering
  • LLM System Architecture (RAG, prompt engineering, agent orchestration, hallucination mitigation at scale)
  • Cloud-Native Deployment & MLOps (AWS primary; Docker, Kubernetes, CI/CD, vector databases, observability)
  • Data Security & Privacy (CPNI, PII)

Other signals

  • setting technical direction for agentic AI delivery
  • building shared components and standards for agentic AI
  • shipping agentic AI to T-Mobile
  • measuring performance on validated ROI across the FDE portfolio