This role pays less than
88%
of similar roles. Most pay
$179,475–$254,750
— the shaded band above.
At the midpoint, this role pays about
$152k
versus about
$217k
for comparable roles.
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
21 open roles
on FindRole.
Listed pay typically runs
$127,800–$214,360
across 21 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 building autonomous AI agent architectures with Retrieval-Augmented Generation capabilities, establishing MLOps standards like CI/CD and containerized serving using Docker and Kubernetes, and fine-tuning Large Language Models. The candidate will utilize Python, SQL, and cloud-native services such as AWS SageMaker or Amazon Bedrock to solve complex problems in conversational AI, intelligent assistants, and task automation. Technical expertise required includes experience with Hugging Face, vector databases, knowledge graphs, and NLP systems like Named Entity Recognition on platforms including AWS, GCP, or Azure.
Lead the architecture, design, and development of enterprise-scale machine learning and Generative AI systems.
Build end-to-end ML pipelines including data ingestion, feature engineering, model training, and deployment using Python and SQL.
Develop autonomous AI agent architectures with Retrieval-Augmented Generation (RAG) for conversational AI and task automation.
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.
Translate complex machine learning concepts into actionable insights for executive leadership and stakeholders.
Mentor data scientists and ML engineers on best practices in GenAI development and production deployment.
What we're looking for
Must have a Master's degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or a related field.
Must have a Bachelor's degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or a related field and 5 years of relevant work experience.
Candidates with a Master's degree must have at least 3 years of relevant work experience.
Experience developing and deploying enterprise-scale applications powered by Large Language Models including API integration, prompt engineering, and response handling.
Experience implementing Retrieval-Augmented Generation (RAG) architectures, including document ingestion, embedding generation, and vector database integration.
Experience designing and deploying NLP systems such as Named Entity Recognition, text classification, and semantic search on cloud platforms.
Experience establishing MLOps/AIOps practices using Docker, Kubernetes, model optimization techniques, and CI/CD pipelines.
Remote (Jersey City, NJ)31 days ago
$142,320–$213,480
Generative AI
Large Language Models (LLMs)
Python
LangChain
VectorRAG
GraphRAG
Agentic AI
Docker
Kubernetes
CI/CD
Jenkins
GitLab
Infrastructure as Code (IaC)
Microservices
SQL
NoSQL
AWS
GCP
Azure
API Design