Principal Engineer, Software – AI Platforms & Network Data

T-Mobile•Bellevue, WA

1d•Onsite

About The Position

The Principal Engineer serves as a senior technical leader responsible for designing, building, and evolving enterprise-scale AI, software, and data platforms that power T-Mobile's Network Data & AI initiatives. This role architects intelligent systems that transform network, device, and customer data into trusted data products, AI-powered solutions, and automated workflows supporting engineering, business operations, and executive decision-making. Working at the intersection of artificial intelligence, software engineering, cloud computing, and distributed systems, the Principal Engineer develops reusable AI services and platform capabilities—including Generative AI, Retrieval-Augmented Generation (RAG), Agentic AI, and enterprise knowledge systems—that accelerate intelligent automation, engineering productivity, and AI adoption across the Network Data & AI organization. This role requires a highly collaborative technical leader who combines hands-on software engineering with strategic architectural thinking to solve complex technical challenges, evaluate emerging technologies, and influence technical direction across multiple engineering teams. Success is measured by delivering scalable, secure, and highly available AI platforms, software solutions, and data products that improve operational efficiency, enable intelligent automation, support strategic business initiatives, and deliver measurable business value.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline with 7+ years of relevant experience, or an advanced degree with 5+ years of relevant experience.
7 – 10 years of technical engineering experience.
Analytical Thinking
Analytics
Cloud Architectures
Cloud Computing
Collaboration
Communication
Customer Service
Leadership
Software Development
Technical Evaluation
Technical Planning
Technical Writing
Java
Python
RAG architectures
Agentic AI design and development
AI/LLM evaluation frameworks
Azure AI Search
LangChain, LangGraph

Nice To Haves

Demonstrated experience designing and delivering enterprise-scale software systems.
Strong software engineering background with experience building scalable, distributed applications.
Experience architecting cloud-native applications and distributed platforms.
Proficiency in Java and Python.
Experience applying modern AI technologies, including Generative AI and Large Language Models, within production software solutions.
Strong analytical, problem-solving, and technical communication skills.
Demonstrated ability to influence technical direction across engineering teams.
Experience mentoring engineers and providing technical leadership.
Experience working with large-scale data platforms, AI-powered applications, analytics platforms, or network data environments is highly desirable.
Certified Artificial Intelligence Professional (CAIP): Certification that validates expertise in designing and implementing AI solutions, including AI modeling and system integration. (Preferred)
Project Management Professional (PMP) Internationally recognized certification that demonstrates proficiency in project management, crucial for overseeing AI integration projects. (Preferred)
Certified Data Scientist (CDS): Certification that endorses skills in data science, crucial for the development and tuning of AI models. (Preferred)

Responsibilities

Lead the architecture, design, and implementation of enterprise-scale AI, software, and data platforms supporting Network Data & AI initiatives.
Design and develop cloud-native, distributed systems that transform large-scale network and enterprise data into trusted data products and AI-powered solutions.
Build reusable AI services utilizing Generative AI, Retrieval-Augmented Generation (RAG), Agentic AI, enterprise knowledge systems, and related technologies.
Develop scalable software solutions using modern programming languages, cloud services, and distributed computing technologies.
Evaluate emerging technologies and establish architectural patterns that improve scalability, reliability, security, and engineering productivity.
Provide technical leadership through architecture reviews, mentoring, technical guidance, and engineering best practices.
Partner with engineering, product, network, and business stakeholders to design solutions for complex technical and business challenges.
Drive innovation through practical application of emerging AI technologies, intelligent automation, and modern software engineering practices.
Produce clear technical documentation, architecture designs, and implementation guidance that support long-term platform evolution.
Remain an active, hands-on engineer, contributing to software design, implementation, technical problem solving, and code reviews.