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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr AI Engineer - **Company:** T-Mobile Us, Inc. - **Location:** Kent, WA, United States - **Experience:** Expert - **Salary:** $133,100.0 - $240,100.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Application Frameworks, Automation of Tests, Microsoft Azure, Cloud Computing, Cloud Engineering, Code Review, Computer Programming, Continuous Integration, Data Integration, DevOps, Distributed Systems, Fault Tolerance, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Performance Tuning, Search Technologies, Software Deployment, Software Engineering, Systems Architecture, Systems Integration, Software Technical Review, Unstructured Data, Web Application Frameworks, Data Logging, Google Cloud, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Deep Learning, Model Validation, Generative AI, Agentic-AI, Git, Fastapi, Containerization, Kubernetes, Information Technology, Low Latency, Deployment Automation, Data Analytics, Data Management, Invoking Functions, Api Design, Restful APIs, Model Context Protocol, Docker - **Published:** October 30, 2026 - **Apply:** https://diversityjobs.com/career/18789829/Sr-Ai-Engineer-Washington-Bellevue ## About the Role * Programming: Strong proficiency in Python, software engineering principles, and object-oriented programming. * API Development: Experience designing, developing, securing, and integrating RESTful APIs using frameworks such as FastAPI or equivalent. * Generative AI and LLMs: Hands-on experience building applications using large language models, prompt engineering, structured outputs, function calling, and tool integration. * Agentic AI: Experience developing AI agents, tool-calling workflows, and orchestration solutions using LangGraph, LangChain, or similar frameworks. * RAG and Data Management: Knowledge of embeddings, vector databases, semantic retrieval, document processing, and data integration. * Cloud Computing: Experience developing and deploying applications on Azure, AWS, or Google Cloud. * Software Engineering and DevOps: Proficiency with Git, CI/CD pipelines, automated testing, and production software development practices. * System Architecture: Ability to design scalable, secure, reliable, and maintainable distributed applications. * Data Analysis and Modeling: Strong analytical skills and experience working with structured and unstructured data. * AI Evaluation: Understanding of model evaluation, performance benchmarking, accuracy measurement, and AI quality assurance. * Collaboration: Strong communication, problem-solving, and cross-functional collaboration skills. PREFERRED: * Experience with Model Context Protocol (MCP) and enterprise AI tool integrations. * Experience with Docker, Kubernetes, and cloud-native deployment tooling for AI workloads. EDUCATION & EXPERIENCE REQUIRED: * Bachelor's Degree plus 5 years of related work experience OR advanced degree with 3 years of related work experience PREFERRED: * Acceptable areas of study include Computer Science, Artificial Intelligence, or Related Field Master's/Advanced Degree Computer Science, Artificial Intelligence, or Data Science * 4+ yrs experience in developing and optimizing AI models for customer service automation using advanced techniques such as prompt engineering and fine-tuning. * 4+ yrs experience in architecting and deploying sophisticated agentic AI systems to enhance reasoning and interaction capabilities in complex workflows. * 4+ yrs experience in collaborating with cross-functional teams to integrate AI-driven enhancements into production systems., * 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. * 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. 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. * At least 18 years of age * Legally authorized to work in the United States ## Description We're seeking a Senior AI Engineer to architect and optimize the agentic AI systems driving the evolution of customer service. You'll design AI that performs with precision and coordinates across models, internal APIs, and third-party tools to create reliable, production-grade workflows and intuitive customer interactions. Using advanced techniques such as prompt engineering, model fine-tuning, evaluation frameworks, and system-level orchestration, you'll improve the accuracy, reliability, and reasoning capabilities of AI across the enterprise. Working closely with engineering, product, and operations teams, you'll bring these innovations to life in resilient, fault-tolerant environments supporting millions of real-world customer conversations. This is an opportunity to help define the AI foundation of the Un-carrier and build intelligent systems that set a new standard for customer experience. If you're passionate about engineering quality, solving complex problems, and making a meaningful customer impact, we invite you to apply. RESPONSIBILITIES * Design, develop, and deploy scalable, production-grade Generative AI applications, LLM-powered solutions, and agentic AI systems to support enterprise business operations and customer service automation. * Architect and implement sophisticated AI agents and multi-agent orchestration workflows using modern frameworks such as LangGraph, LangChain, or equivalent technologies. * Develop Retrieval-Augmented Generation (RAG) solutions leveraging embeddings, vector databases, semantic search, and enterprise knowledge sources. * Build and integrate RESTful APIs, Model Context Protocol (MCP) connectors, and enterprise data services to enable secure AI interactions with internal and external systems. * Optimize AI performance through prompt engineering, model selection, retrieval optimization, fine-tuning, and efficient inference strategies to enhance customer service automation. * Design and implement AI evaluation frameworks to measure accuracy, hallucination rates, reliability, latency, throughput, and cost efficiency, ensuring alignment with strategic objectives and customer satisfaction goals. * Develop cloud-native AI solutions using Azure, AWS, or equivalent platforms, incorporating containerization, automated deployment, and scalable architectures. * Implement security, governance, and responsible AI practices, including authentication, authorization, data privacy, prompt injection protection, and enterprise compliance requirements. * Establish observability, logging, monitoring, and troubleshooting capabilities for AI applications and agentic workflows in production environments. * Collaborate with cross-functional engineering, architecture, product, cybersecurity, and business teams to ensure seamless integration of AI-driven enhancements into production systems. * Conduct technical design reviews, participate in code reviews, mentor engineers, and contribute to reusable engineering standards and best practices. * Research and evaluate emerging AI technologies, open-source frameworks, and industry advancements to stay current with the latest methodologies applicable to customer service and to identify opportunities for innovation and continuous improvement. * Perform other duties and projects as assigned by business management.