Senior ML Engineer — GenAI & MLOps Lead (Hybrid)

T-Mobile; Frisco (TX); Hybrid; USD 147,000 - 157,000; Full time

Benefits offered by this job

Employee stock grants

401(k)

Paid time off and holidays

Job summary

T-Mobile USA, Inc. is seeking a Sr Engineer, Machine Learning in Frisco, TX to lead enterprise GenAI system development across data ingestion, training, deployment, and governance. You will architect pipelines, select cloud services, and drive production readiness for large-scale models.

The role emphasizes leadership, collaboration with cloud providers, and ongoing evaluation of GenAI advancements to shape the AI roadmap while fostering best practices in MLOps and deployment.

Qualifications

• Master’s degree or higher in a related field with relevant ML work experience.
• Experience with enterprise-scale ML deployments and production readiness.
• Proven ability to design and optimize GenAI architectures at scale.

Responsibilities

• Lead architecture, design, and development of enterprise-scale ML and GenAI systems.
• Architect end-to-end ML pipelines using Python, SQL, and cloud services.
• Develop autonomous AI agent architectures with RAG for enterprise apps.
• Establish MLOps standards including CI/CD, model versioning, and monitoring.
• Evaluate new GenAI technologies to influence product roadmap.
• Mentor data scientists and ML engineers.

Skills

LLM deployment

RAG architectures

NLP & semantic

MLOps / AIOps

Fine-tuning LLMs

Knowledge graphs

Education

Master's degree in CS/Stats/Info Systems/ML

Bachelor's degree with 5 years experience

Tools

Docker

Kubernetes

Cloud platforms (AWS/GCP/Azure)

Job description

T-Mobile USA, Inc. is seeking a Sr Engineer, Machine Learning in Frisco, TX to lead enterprise GenAI system development across data ingestion, training, deployment, and governance. You will architect pipelines, select cloud services, and drive production readiness for large-scale models.

The role emphasizes leadership, collaboration with cloud providers, and ongoing evaluation of GenAI advancements to shape the AI roadmap while fostering best practices in MLOps and deployment.