AI & Modeling Relations Manager

T-Mobile USA•Frisco, TX

4d•Onsite

About The Position

The AI & Modeling Relations Manager is the primary liaison between the business and T-Mobile's AI Center of Excellence (COE), responsible for translating enterprise AI capabilities into adopted, governed, and cost-effective solutions for the business unit. This role requires fluency in modern AI technology and terminology — large language models (LLMs), retrieval-augmented generation (RAG), agentic AI, prompt engineering, machine learning operations (MLOps)/large language model operations (LLMOps), and responsible AI practices — to credibly partner with COE architects and data scientists while advocating for business needs. The manager owns the intake, evaluation, governance alignment, and financial stewardship of AI initiatives sourced through or coordinated with the COE, ensuring the business unit adopts AI responsibly, efficiently, and at the pace the organization can absorb.

Requirements

Bachelor's Degree plus 5 years of related work experience OR Advanced degree with 3 years of related experience
Acceptable areas of study include Finance, Economics, Mathematics, Statistics or related field
5+ years of experience in technology program/product management, vendor management, or a related cross-functional leadership role.
Demonstrated working knowledge of AI/ML concepts and terminology, including large language models (LLMs), generative AI, machine learning, natural language processing (NLP), retrieval-augmented generation (RAG), prompt engineering, and agentic AI.
Experience managing relationships with technical teams, centers of excellence, or shared services organizations.
Strong communication skills with the ability to translate technical AI concepts for non-technical business stakeholders and vice versa.
Key AI Literacy Expectations: Foundation models & LLMs — GPT, Claude, Gemini, Llama — and how they're selected/deployed.
Generative AI vs. predictive/traditional ML vs. agentic AI.
Prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
Embeddings and vector databases.
MLOps/LLMOps, model monitoring, and model drift.
Responsible AI: bias, fairness, explainability, hallucination, guardrails.
AI governance and risk frameworks, data classification, and privacy-by-design.
Inference cost, token usage, and compute optimization.
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

Experience working directly with or within an AI/ML Center of Excellence, data science team, or enterprise AI governance function.
Familiarity with responsible AI/model risk frameworks (e.g., NIST AI RMF, ISO/IEC 42001) and AI governance practices.
Working knowledge of MLOps/LLMOps concepts, model evaluation, fine-tuning, embeddings, and vector databases.
Experience with the AI vendor and platform landscape (e.g., OpenAI, Anthropic, Google, Microsoft Copilot, AWS Bedrock) sufficient to support evaluation conversations.
PMP, Agile/Scrum, or similar program management certification.

Responsibilities

AI COE Relationship Management: Serve as the single point of contact between the business unit and the AI Center of Excellence, managing intake of AI use cases, prioritization, and delivery timelines.
Build and maintain a trusted working relationship with COE leadership, architects, data scientists, and ML engineers to ensure business priorities are represented in the COE roadmap.
Translate business problems into well-formed AI use case briefs and translate COE technical capabilities and constraints back into business-friendly guidance.
Facilitate regular governance/steering meetings, roadmap reviews, and escalation paths between the business unit and the COE.
AI Governance & Compliance: Ensure all AI initiatives sourced through the COE comply with enterprise responsible AI principles, model risk management standards, and data classification/privacy requirements.
Partner with Legal, Privacy, Security, and Risk teams to support AI impact assessments, model documentation, and audit readiness.
Maintain a current inventory of AI/ML models and tools in use within the business unit, including their approval and governance status.
AI Vendor & Tool Evaluation: Partner with the COE to evaluate new AI platforms, foundation models, and third-party AI vendors (e.g., LLM providers, vector database platforms, AI agent frameworks) against business requirements.
Coordinate proof-of-concept (POC) and pilot evaluations, defining success criteria, evaluation rubrics, and go/no-go recommendations.
Stay current on the evolving AI vendor landscape (foundation model providers, AI copilots, agentic platforms) and advise the business on build-vs-buy decisions in partnership with the COE.
Business Unit AI Enablement: Identify and prioritize high-value AI use cases within the business unit, in partnership with COE data scientists and solution architects.
Drive adoption of COE-delivered AI capabilities (copilots, chatbots, predictive models, automation agents) through training, change management, and communication.
Act as the business unit's champion for AI literacy, helping teams understand what AI can (and cannot) responsibly do.