AI Platform Engineer – Safe GenAI & RAG

T-Mobile; Frisco (TX); On-site; USD 110,000 - 199,000; Full time

Benefits offered by this job

Stock grants and RSUs

Employee stock purchase plan

401(k) with company match

Paid time off and holidays

Medical, dental, vision coverage

Tuition assistance

Job summary

T-Mobile is seeking a skilled AI engineering professional to help scale our GenAI platforms for enterprise use, ensuring secure rollout, policy compliance, and rapid adoption. You will own evaluation harnesses, model risk and tools integration across teams.

You will design RAG pipelines, fine-tune models, and collaborate with product and engineering partners to deliver measurable improvements in customer service automation and operational efficiency.

Qualifications

• Data Management/RAG; Vector Databases/Embeddings
• Prompt Engineering - LLM orchestration and RAG workflow design
• Python - LLM Orchestration Frameworks
• Cloud Computing - RAG deployment, LLM infra

Responsibilities

• Design and implement retrieval-augmented generation (RAG) pipelines to ground AI responses in enterprise knowledge sources
• Develop AI models to enhance customer service automation capabilities with measurable improvements
• Apply prompt engineering and fine-tuning techniques to optimize AI system performance
• Create AI tools that support integration of AI-driven improvements into operational frameworks
• Collaborate with cross-functional teams to ensure effective integration and functionality of AI systems
• Design AI systems that improve interaction capabilities within complex service workflows
• Contribute to strategic AI initiatives that advance organizational goals of customer satisfaction and efficiency
• Also responsible for other duties/projects as assigned by business management as needed

Job description

T-Mobile is seeking a skilled AI engineering professional to help scale our GenAI platforms for enterprise use, ensuring secure rollout, policy compliance, and rapid adoption. You will own evaluation harnesses, model risk and tools integration across teams.

You will design RAG pipelines, fine-tune models, and collaborate with product and engineering partners to deliver measurable improvements in customer service automation and operational efficiency.