Sr AI Engineer

Responsibilities

• Buils 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
• Maintains 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

Requirements

• 4+ years developing and deploying machine learning models, particularly in the context of AI-driven customer service automation
• 4+ years experience with advanced AI techniques such as prompt engineering, fine-tuning, and creating AI tools and workflows
• 4+ years 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 generation)
• 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

Qualifications

• Bachelor’s degree in computer science, Artificial Intelligence, or equivalent experience
• Master's/Advanced Degree in Computer Science or Artificial Intelligence Preferred
• At least 18 years of age
• Legally authorized to work in the United States