Sr Engineers, Machine Learning

At T-Mobile, we invest in YOU!  Our Total Rewards Package ensures that employees get the same big love we give our customers.  All team members receive a competitive base salary and compensation package - this is Total Rewards. Employees enjoy multiple wealth-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year-round money coaches. That’s how we’re UNSTOPPABLE for our employees! Position summary T-Mobile is America’s supercharged Un-carrier, delivering an advanced 4G LTE and transformative nationwide 5G network that will offer reliable connectivity for all. Sr Engineers, Machine Learning located in Frisco, Texas will enable systems for coding, deploying, and maintaining large-scale machine learning models throughout their lifecycle. Position duties and responsibilities include, but are not limited to: Lead the architecture, design, and development of enterprise-scale machine learning and Generative AI systems, ensuring alignment with T-Mobile's strategic business objectives. Architect end-to-end ML pipelines including data ingestion, feature engineering, model training, optimization, and deployment using Python, SQL, and cloud-native ML services such as AWS SageMaker or Amazon Bedrock. Design and implement production AI systems on cloud platforms (AWS, GCP, or Azure), making strategic technology selections for compute, storage, and inference infrastructure. Develop and deploy autonomous AI agent architectures with Retrieval-Augmented Generation (RAG) capabilities for conversational AI, intelligent assistants, and enterprise task-automation applications. Establish and drive organization-wide standards for MLOps practices including CI/CD pipelines, model versioning, monitoring, and governance to ensure production reliability and compliance. Evaluate emerging Generative AI technologies, benchmark large language models, and provide technical recommendations that influence T-Mobile's AI product roadmap. Translate complex machine learning concepts and model behaviors into actionable insights for executive leadership and cross-functional business stakeholders. Mentor and provide technical leadership to teams of data scientists and ML engineers, fostering best practices in GenAI development and production deployment. Collaborate with industry partners, cloud providers, and research communities to identify and adopt cutting-edge AI advancements. Drive the successful delivery of advanced GenAI solutions including large language model applications, conversational AI systems, and intelligent automation platforms. Skill requirements:

(1) Experience developing and deploying enterprise-scale applications powered by Large Language Models, including API integration with LLM providers (including OpenAI, Anthropic, Azure OpenAI, or open-source models via Hugging Face), prompt engineering, and response handling for production user-facing systems.

(2) Experience implementing Retrieval-Augmented Generation (RAG) architectures in LLM applications, including document ingestion pipelines, embedding generation, vector database integration, and semantic retrieval systems for knowledge-based applications.

(3) Experience designing and deploying NLP and semantic understanding systems including Named Entity Recognition (NER), text classification, semantic search, and entity disambiguation on cloud platforms (AWS, OCI, or Azure).

(4) Experience establishing MLOps/AIOps practices for production machine learning systems, including containerized model serving infrastructure using Docker and Kubernetes for LLM inference at scale, model optimization techniques (quantization, distillation, or runtime optimization), observability instrumentation, and CI/CD pipeline implementation.

(5) Experience fine-tuning Large Language Models using transfer learning, few-shot learning, or prompt engineering techniques for domain-specific