Sr. AI Engineer

T-Mobile•Bellevue, WA

1d•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 join the Un-carrier movement and help build the AI experiences shaping the future of customer service? Join IntentCX, the team driving T-Mobile’s next generation of AI-powered customer experience. In this role, you will help architect and optimize advanced agentic AI systems that improve reasoning, automation, and customer interactions across complex service workflows. You will apply prompt engineering, fine-tuning, AI tooling, and workflow design to strengthen the reliability, accuracy, and efficiency of models used in customer service operations. This is an opportunity to take deeper ownership of AI solutions that matter, help protect and grow T-Mobile’s internal AI intellectual property, and shape how AI scales across one of the most customer-focused brands in the industry. We pride ourselves on encouraging a culture of innovation, advocating for agile methodologies, and promoting transparency in all that we do. Join us in embodying the spirit of the 'Un-carrier' and make a tangible impact! Our team is dynamic where no day is the same, and we are diverse and inclusive passionate about growth and transformation. If you're up to the challenge, apply today!

Requirements

Data Analysis Expertise in analyzing data to derive insights and inform AI model improvements. (Required)
Problem Solving Ability to troubleshoot and resolve complex technical issues related to AI systems. (Required)
Communication Strong communication skills to effectively collaborate with cross-functional teams and articulate technical concepts. (Required)
Cross Functional Relationships Ability to work effectively with various teams including engineering, product management, and customer service. (Required)
Bachelor's degree plus 3 years of related work experience OR advanced degree with 1 year of related work experience
Acceptable areas of study include Computer Science, Artificial Intelligence, or Related Field
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

Experience building or operating LLM applications, conversational AI, agentic platforms, AI workflow automation, or production AI systems.
Experience with RAG, knowledge-layer integration, knowledge graphs, GraphQL APIs, vector-backed services, or contract-based data and service integrations.
Experience with multi-agent frameworks, orchestration systems, handoffs, tool/function calling, or agent runtime infrastructure.
Experience with AI evaluation approaches such as golden test sets, regression sets, simulation, LLM-as-judge, production conversation review, trace sampling, or release gates.
Experience with observability or evaluation platforms such as OpenTelemetry, Grafana, Splunk, Datadog, LangSmith, Weights and Biases, or equivalent tools.
Experience with responsible AI, AI safety, privacy reviews, policy enforcement, NIST AI RMF, OWASP LLM risks, or red-team testing.
Experience with customer service AI, real-time conversational agents, contact center systems, digital service platforms, or large-scale customer interaction platforms.
Experience with Python, API design, CI/CD, containers, Kubernetes, production service design, or release management.
4+ yrs experience in developing and optimizing AI models for customer service automation using advanced techniques such as prompt engineering and fine-tuning. (Preferred)
4+ yrs experience in architecting and deploying sophisticated agentic AI systems to enhance reasoning and interaction capabilities in complex workflows. (Preferred)
4+ yrs experience in collaborating with cross-functional teams to integrate AI-driven enhancements into production systems. (Preferred)
AI Evaluation Ability to assess model, prompt, workflow, and agent performance using measurable quality, reliability, accuracy, latency, and customer impact indicators. (Preferred)
Production AI Systems Ability to support AI workflow implementation, observability, release readiness, and issue resolution for customer-facing systems. (Preferred)
Responsible AI Ability to apply guardrails and controls that reduce customer, privacy, safety, and operational risk in AI-enabled experiences. (Preferred)
Master's/Advanced Degree Computer Science, Artificial Intelligence, or Data Science (Preferred)
Certified Analytics Professional (CAP): Certification that endorses an individual's expertise in analytics, including the ability to transform complex data into valuable insights and actions, which is crucial for optimizing AI models in customer service automation. (Preferred)
NVIDIA Deep Learning AI Certification: This certification demonstrates proficiency in designing and deploying deep learning AI applications, relevant for enhancing AI model performance in complex systems like customer service workflows. (Preferred)
Certified Data Scientist (CDS): Certification that validates the skills required to analyze data scientifically and use it for operational improvements, which supports the development and optimization of AI tools and workflows. (Preferred)

Responsibilities

Optimize AI performance through prompt engineering, fine-tuning, structured outputs, and model evaluation to enhance customer service automation.
Develop AI tools and workflows to support implementation of advanced AI systems within customer service operations.
Architect sophisticated agentic AI systems, including routing, handoffs, context propagation, tool selection, and fallback behavior, to improve reasoning and interaction capabilities across complex workflows.
Collaborate with cross-functional teams to ensure seamless integration of AI-driven enhancements into production systems.
Evaluate the reliability, accuracy, and efficiency of AI models using test sets, production feedback, regression checks, and quality measures to ensure alignment with strategic objectives and customer satisfaction.
Support responsible AI controls and guardrails for customer-facing AI systems, including hallucination risk, unsafe escalation paths, prompt injection, tool misuse, and privacy considerations.
Partner with engineering, product, customer service, data, quality, security, privacy, and operations teams to identify implementation needs, integration risks, and production quality improvements.
Troubleshoot AI behavior, latency, workflow, tool, or production quality issues and contribute to root cause analysis and remediation planning.
Conduct ongoing research to stay updated with the latest AI technologies and methodologies applicable to customer service.
Mentor engineers through design guidance, code review, technical feedback, and practical examples appropriate to project scope.
Also responsible for other duties/projects as assigned by business management as needed.