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This listing doesn't post a salary. Most similar roles pay
$192,050–$254,750.
Based on 240 similar postings.
Employer
About T-Mobile
T-Mobile US is the second-largest wireless carrier in the United States, providing wireless voice, messaging, and data services under the T-Mobile and Metro by T-Mobile brands. Industry: Wireless Telecommunications
T-Mobile currently has
72 open roles
on FindRole.
Listed pay typically runs
$138,996–$228,650
across 32 roles with salary data.
Sr Engineers, Machine Learning will join the team to enable systems for coding, deploying, and maintaining large-scale machine learning models throughout their lifecycle. The role involves leading the architecture and development of enterprise-scale machine learning and Generative AI systems, including end-to-end pipelines for data ingestion, feature engineering, and model training. Key responsibilities include developing autonomous AI agent architectures with Retrieval-Augmented Generation capabilities, establishing MLOps standards for CI/CD and model versioning, and benchmarking large language models. Candidates must utilize Python, SQL, and cloud-native services like AWS SageMaker or Amazon Bedrock, alongside Docker and Kubernetes. The work focuses on solving technical problems related to production AI systems, NLP, semantic understanding, and knowledge graph architectures to deliver advanced conversational AI and intelligent automation platforms.
Lead the architecture, design, and development of enterprise-scale machine learning and Generative AI systems.
Architect end-to-end ML pipelines for data ingestion, feature engineering, model training, and deployment using Python and SQL.
Design and implement production AI systems on cloud platforms including AWS, GCP, or Azure.
Develop and deploy autonomous AI agent architectures with Retrieval-Augmented Generation (RAG) capabilities.
Establish organization-wide MLOps standards for CI/CD pipelines, model versioning, monitoring, and governance.
Evaluate emerging Generative AI technologies and benchmark large language models to influence the product roadmap.
Mentor data scientists and ML engineers on best practices for GenAI development and production deployment.
Translate complex machine learning concepts and model behaviors into actionable insights for executive leadership.
What we're looking for
Master’s degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or related field and 3 years of relevant work experience.
Bachelor’s degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or related field and 5 years of relevant work experience.
Experience developing and deploying enterprise-scale applications powered by Large Language Models using providers like OpenAI, Anthropic, or Hugging Face.
Experience implementing Retrieval-Augmented Generation (RAG) architectures, including document ingestion, embedding generation, and vector database integration.
Experience designing and deploying NLP systems including NER, text classification, and semantic search on cloud platforms like AWS, OCI, or Azure.
Experience establishing MLOps/AIOps practices using Docker and Kubernetes for containerized model serving, optimization, and CI/CD pipelines.
Experience fine-tuning Large Language Models using transfer learning, few-shot learning, or prompt engineering for domain-specific applications.
Machine Learning
Deep Learning
Python
MATLAB
PyTorch
Tensor flow
Keras
scikit-learn
C
C++
Digital Signal Processing
Numerical Modeling
Optimization
Neural Networks
CNN
RNN
Embedded Systems
Statistical Signal Processing
Algorithm Development
Machine Learning
Deep Learning
Generative AI
Optimization Algorithms
Python
C++
Java
Go
C
Spark
Hive
Causal Modeling
Reinforcement Learning
Representation Learning
Large-scale Data Systems
Production-ready Algorithmic Systems