At T-Mobile, the business problem is to redefine the future of customer service with the power of AI by optimizing and orchestrating AI models that power T-Mobile's customer service automation, enhancing model outputs, and tackling complex challenges in customer service to directly impact customer satisfaction and set new standards for reliability and efficiency.
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
- 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).
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.
Other
- 4+ collaborating with cross-functional teams to integrate AI systems into production environments
- 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.
