Sr AI Engineer

T-Mobile•Bellevue, WA

1d•$133,100 - $240,100•Onsite

About The Position

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! Are you ready to redefine the future of customer service with the power of AI? As a Sr AI Engineer on the IntentCX team, you will drive the optimization and orchestration of AI models that power T-Mobile’s customer service automation. Your role will focus on enhancing model outputs by leveraging the latest in prompt engineering and optimization, fine-tuning, and agentic AI systems. You’ll design advanced tools and workflows that empower AI to think, reason, and respond in ways that feel genuinely human, tackling the most complex challenges in customer service. Partnering with diverse teams across the company, you’ll see your innovations move swiftly from concept to reality, directly impacting customer satisfaction and setting new standards for reliability and efficiency. This role is ideal for an LLM expert and AI systems builder who thrives on leveraging prompt engineering, retrieval-augmented generation (RAG), and function calling to improve AI-driven customer interactions at scale. We pride ourselves on encouraging a culture of innovation, advocating for agile methodologies, and promoting clarity in all that we do. Join us in embodying the spirit of the 'Un-carrier' and make a tangible impact! If you are passionate about driving excellence and want to make a significant impact, apply today!

Requirements

4+ yrs developing and deploying machine learning models, particularly in the context of AI-driven customer service automation.
4+ yrs experience with advanced AI techniques such as prompt engineering, fine-tuning, and creating AI tools and workflows
4+ collaborating with cross-functional teams to integrate AI systems into production environments
Proficiency in Python and AI development frameworks for building scalable AI applications.
Experience with operational excellence practices and observability tools (e.g., Weights & Biases, Splunk, Datadog) for monitoring, logging, and troubleshooting AI systems in production.
Experience with project management tools and agile methodologies (e.g., Jira, Azure DevOps) to plan, track, and deliver AI initiatives efficiently in cross-functional environments.
Experience in LLM fine-tuning and prompt engineering (e.g., OpenAI APIs, Hugging Face, Anthropic Claude, Google Gemini)
Experience with AI orchestration tools (e.g., LangChain, LlamaIndex, vector databases for retrieval augmented genetation).
Hands-on knowledge of function calling and API-based reasoning models (e.g., using structured outputs to drive automated workflows).
Familiarity with RAG pipelines and vector database retrieval for augmenting AI responses.
Understanding of multi-agent architectures and best practices in agentic AI design.
Experience with real-world AI evaluation techniques, including golden sets, synthetic data generation, and interactive testing.
Ability to collaborate across teams, working with engineers, product managers, and conversational designers to refine AI-driven solutions.
Show Don’t Tell Mentality –Demonstrates AI improvements through tangible results, prototypes, and real-world impact rather than just theoretical discussions. Uses data, examples, and testing to validate optimizations.
Problem-Solving & Analytical Thinking – Ability to break down complex AI challenges, diagnose model behavior issues, and implement innovative solutions.
Collaboration & Cross-Functional Communication – Works effectively with engineers, product managers, designers, and business leaders to align AI solutions with business needs.
Adaptability & Continuous Learning – Stays up to date with the latest AI advancements, experimenting with new techniques to refine model performance.
User-Centric Mindset – Understands customer needs and prioritizes AI improvements that enhance usability, accuracy, and overall customer experience.
Attention to Detail – Ensures high precision in AI model optimizations, prompt engineering, and workflow automation.
Strategic Thinking – Balances short-term model improvements with long-term AI architecture planning to scale IntentCX effectively.
Ownership & Accountability – Takes responsibility for AI model performance, proactively identifying and resolving challenges before they impact users.
Bachelor’s degree in computer science, Artificial Intelligence, or equivalent experience.

Nice To Haves

Master's/Advanced Degree in Computer Science or Artificial Intelligence Preferred

Responsibilities

Builds agentic AI systems that accomplish complex tasks by invoking AI models as well as internal and third-party tools using APIs, ensuring seamless data flow in production environments
Optimizes performance of agentic AI systems through innovative techniques such as prompt engineering, fine-tuning and reinforcement learning using T-Mobile’s customer interaction data
Develops AI tools, workflows, and middleware to enhance model capabilities, such as structured reasoning, multi-step task execution, and improved contextual memory.
Implements retrieval-augmented generation (RAG) techniques to ensure AI responses are contextually accurate and grounded in real-time data.
Collaborates in a highly matrixed environment with backend engineers, business experts and conversation designers to ensure AI-driven enhancements are effectively integrated into production environments
Tracks success metrics that aligns with business requirements and continuously evaluate and improve model quality based on those metrics
Develops internal tooling and automation to streamline AI deployment, evaluation, and self-improvement mechanisms.
Monitors real-world AI performance and proactively iterates on model behavior based on live interaction data.
Stays up-to-date with latest LLM advancements in prompt design, prompt optimization, few-shot learning, Tool integration protocols like MCP and AI orchestration frameworks like Agent SDK