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You’ll Do:</strong></p><ul><li>Design, build, and deploy production-grade AI chatbots and conversational agents for customer service automation, leveraging LLMs and orchestration frameworks such as LangChain, LlamaIndex, or similar.&nbsp;</li><li>Develop and maintain agentic AI systems using frameworks such as Google Agent Development Kit (ADK), OpenAI Agents SDK, AutoGen, or CrewAI — including tool use, memory, planning, and multi-agent coordination.&nbsp;</li><li>Integrate with LLM APIs (OpenAI, Anthropic, Gemini, Azure OpenAI) to build robust, scalable AI-powered features including chat interfaces, function calling, structured outputs, and retrieval-augmented generation (RAG).&nbsp;</li><li>Optimize AI performance through prompt engineering, few-shot learning, chain-of-thought reasoning, and fine-tuning to enhance accuracy and reliability in customer service automation.&nbsp;</li><li>Architect sophisticated agentic AI systems to improve reasoning and interaction capabilities across complex, multi-step workflows including task decomposition, agent handoffs, and tool orchestration.&nbsp;</li><li>Develop AI tools and workflows to support implementation of advanced AI systems within customer service operations, including evaluation frameworks and observability pipelines.&nbsp;</li><li>Collaborate with cross-functional teams to ensure seamless integration of AI-driven enhancements into production systems.&nbsp;</li><li>Evaluate the reliability, accuracy, and efficiency of AI models to ensure alignment with strategic objectives and customer satisfaction using metrics such as BLEU, ROUGE, LLM-as-judge, and task completion rates.&nbsp;</li><li>Conduct ongoing research to stay current with the latest AI technologies including emerging agent frameworks, model releases, and methodologies applicable to customer service.&nbsp;</li><li>Produce technical documentation and design specifications including workflow logic, integration patterns, API specifications, catalog structures, change models, data flows, and operational runbooks to support long-term maintainability and engineering alignment.&nbsp;</li></ul><p><strong>What You’ll Bring:</strong></p><ul><li>Bachelor’s Degree in Computer Science or Engineering (Required)&nbsp;</li><li>4–7 years of technical engineering experience, including hands-on development.</li><li>4–7 years of Full Stack development (Required).</li><li>4–7 years of experience building and deploying AI-powered applications including chatbots, virtual assistants, and conversational agents in production environments (Required).</li><li>4–7 years of hands-on experience with LLM APIs such as OpenAI, Anthropic Claude, Google Gemini, or Azure OpenAI — including function calling, embeddings, streaming, and tool use (Required).</li><li>3–5 years of experience developing agentic AI systems using frameworks such as Google ADK, OpenAI Agents SDK, LangChain Agents, AutoGen, CrewAI, or similar (Preferred).</li><li>3–5 years of experience developing and optimizing AI models for customer service automation using prompt engineering, fine-tuning, and RAG pipelines (Preferred).</li><li>4–7 years of experience collaborating with cross-functional teams to integrate AI-driven enhancements into production systems (Preferred)&nbsp;</li><li>Strong communication skills (Required).</li><li>Applied AI Development: Hands-on experience building chatbots, AI agents, and LLM-integrated applications using frameworks such as Google ADK, OpenAI Agents SDK, LangChain, LlamaIndex, or AutoGen (Required)&nbsp;</li><li>LLM Integration: Proficiency with OpenAI, Anthropic, and Google AI APIs including function/tool calling, structured outputs, streaming, embeddings, and RAG architectures (Required)&nbsp;</li><li>Agentic Systems Design: Ability to architect multi-agent systems with tool use, memory management, agent-to-agent handoffs, and state management across complex workflows (Required).</li><li>Prompt Engineering: Expertise in prompt design patterns including zero-shot, few-shot, chain-of-thought, ReAct, and system prompt optimization (Required).</li><li>Data Analysis: Expertise in analyzing data to derive insights and inform AI model improvements (Required)&nbsp;</li><li>Problem Solving: Ability to troubleshoot and resolve complex technical issues related to AI systems including hallucinations, latency, context management, and tool reliability.</li><li>Ability to work effectively with various teams including engineering, product management, and customer service (Required)&nbsp;</li><li>Technical writing and documentation experience (Required)&nbsp;</li><li>Familiarity with Agile practices to adapt to rapidly changing technical requirements and environments&nbsp;</li><li>MLOps/AI Observability: Familiarity with LLM tracing, evaluation pipelines, and monitoring tools such as LangSmith, Weights &amp; Biases, or Helicone (Preferred)&nbsp;</li><li>Vector Databases: Experience with vector stores such as Pinecone, Weaviate, pgvector, or Chroma for RAG and semantic search applications (Preferred)&nbsp;</li><li>Capability to envision and execute strategies that enhance AI-driven customer service solutions&nbsp;</li><li>Creative thinking skills to develop new and innovative solutions within the AI space&nbsp;</li><li>Understanding of telecommunications systems and technologies to better integrate AI solutions&nbsp;</li><li>ServiceNow knowledge and hands-on experience (Preferred).</li><li>Customer-centric mindset with experience supporting operational teams (Required).</li></ul><p><strong>Must Have Skills:</strong></p><ul><li>Bachelor’s Degree in Computer Science or Engineering (Required).</li><li>4–7 years of technical engineering experience, including hands-on development.</li><li>4–7 years of Full Stack development (Required).</li><li>4–7 years of experience building and deploying AI-powered applications including chatbots, virtual assistants, and conversational agents in production environments (Required).</li><li>4–7 years of hands-on experience with LLM APIs such as OpenAI, Anthropic Claude, Google Gemini, or Azure OpenAI — including function calling, embeddings, streaming, and tool use (Required)&nbsp;</li><li>3–5 years of experience developing agentic AI systems using frameworks such as Google ADK, OpenAI Agents SDK, LangChain Agents, AutoGen, CrewAI, or similar (Preferred)&nbsp;</li><li>3–5 years of experience developing and optimizing AI models for customer service automation using prompt engineering, fine-tuning, and RAG pipelines (Preferred).</li><li>4–7 years of experience collaborating with cross-functional teams to integrate AI-driven enhancements into production systems (Preferred)&nbsp;</li><li>Applied AI Development: Hands-on experience building chatbots, AI agents, and LLM-integrated applications using frameworks such as Google ADK, OpenAI Agents SDK, LangChain, LlamaIndex, or AutoGen (Required)&nbsp;</li><li>LLM Integration: Proficiency with OpenAI, Anthropic, and Google AI APIs including function/tool calling, structured outputs, streaming, embeddings, and RAG architectures (Required)&nbsp;</li><li>Agentic Systems Design: Ability to architect multi-agent systems with tool use, memory management, agent-to-agent handoffs, and state management across complex workflows (Required).</li><li>Prompt Engineering: Expertise in prompt design patterns including zero-shot, few-shot, chain-of-thought, ReAct, and system prompt optimization (Required).</li><li>Data Analysis: Expertise in analyzing data to derive insights and inform AI model improvements (Required)&nbsp;</li><li>Problem Solving: Ability to troubleshoot and resolve complex technical issues related to AI systems including hallucinations, latency, context management, and tool reliability.</li><li>Ability to work effectively with various teams including engineering, product management, and customer service (Required)&nbsp;</li><li>Technical writing and documentation experience 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T-Mobile:</strong></p><p>T-Mobile US, Inc. (NASDAQ: TMUS), headquartered in Bellevue, Washington, is America’s supercharged Un-carrier, connecting millions through its strong nationwide network and flagship brands, T-Mobile and Metro by T-Mobile. Customers benefit from an unmatched combination of value, quality, and exceptional service experience.</p><p><strong>TMUS Global Solutions:</strong></p><p>TMUS Global Solutions is a world-class technology powerhouse accelerating the company’s global digital transformation. With a culture built on growth, inclusivity, and global collaboration, the teams here drive innovation at scale, powered by bold thinking.</p><p><strong>About the Role:</strong></p><p>This role is essential for building and enhancing artificial intelligence (AI) models and systems that support customer service automation within the organization. The engineer will design, develop, and deploy applied AI solutions — including conversational chatbots, multi-step AI agents, and agentic workflows — using modern AI frameworks and SDKs. The role focuses on optimizing AI performance through prompt engineering, fine-tuning, and LLM integration, as well as architecting advanced agentic AI systems to improve reasoning and interaction capabilities across complex workflows. Success is measured by the reliability, accuracy, and efficiency of AI models meeting strategic objectives and customer satisfaction goals. The work directly impacts organizational AI capabilities and improves customer service operations through seamless integration of AI enhancements.</p><p>The engineer collaborates with cross-functional teams to streamline operational processes, reduce manual effort, strengthen service reliability, and improve user experience for frontline agents, support teams, and business partners. 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