Sr Software Engineer, Agentic AI

T-Mobile•Atlanta, GA

1d•Onsite

About The Position

This role is responsible for designing, developing, and deploying scalable software and Agentic AI solutions that support intelligent customer engagement across mobile, web, and support platforms. The engineer will build AI-enabled applications, APIs, distributed services, and orchestration layers integrating LLMs, conversational AI, and enterprise systems into customer-facing and operational workflows. The role involves collaborating with engineering and platform teams to develop cloud-native microservices, distributed systems, and AI-driven workflows using modern technologies such as Kubernetes, virtualization, big data platforms, and AI orchestration frameworks. The engineer will contribute to systems supporting conversational and agentic AI capabilities across voice and text channels. This position requires strong technical judgment to resolve complex issues, contribute to software design decisions, and support highly available, scalable, and secure AI-enabled systems operating at scale. Success is measured by delivering reliable, extensible software solutions that improve operational efficiency and customer experience through intelligent AI-driven capabilities.

Requirements

Bachelor's Degree plus 5 years of related work experience OR Advanced degree with 3 years of related experience (Required)
Acceptable areas of study include Computer Science, Software Engineering, Information Management or equivalent experience in field (Required)
4-7 years Technical engineering experience. (Required)
Strong communication, collaboration, and customer-focused problem-solving skills
Strong analytical, troubleshooting, and technical documentation abilities
Experience developing scalable software applications and AI-enabled services using Python, Java, or C++
Experience with cloud-native distributed systems, APIs, microservices, and real-time integration platforms
Familiarity with LLMs, conversational AI, agentic AI workflows, and AI orchestration frameworks
Experience building AI-driven automation, RAG solutions, and enterprise AI integrations
Understanding of scalability, reliability, observability, and secure software engineering best practices for production AI systems
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

Experience designing and deploying cloud-native AI solutions using Docker, Kubernetes, and scalable microservices architectures
Experience building and integrating AI-enabled applications, conversational AI platforms, agentic AI workflows, and enterprise automation systems
Strong familiarity with Large Language Models (LLMs), prompt engineering, tool orchestration, and AI-assisted development workflows
Experience supporting highly available, scalable, and production-grade distributed systems and AI platforms
Experience developing or supporting voice and text-based customer engagement and conversational AI solutions
Familiarity with AI orchestration frameworks, RAG architectures, and real-time AI integration patterns for enterprise applications

Responsibilities

Design, develop, test, and deploy scalable software and Agentic AI solutions to support enterprise automation, intelligent workflows, and customer engagement platforms
Build and enhance AI-enabled applications, backend services, APIs, and integration components using modern software engineering and cloud-native best practices
Develop and implement multi-step AI workflows, orchestration logic, and agent-based systems leveraging LLMs, RAG architectures, and AI automation frameworks
Contribute to the design of microservices and distributed systems supporting real-time voice and text-based customer interactions at scale
Collaborate with cross-functional engineering, AI, platform, and product teams to deliver secure, reliable, and high-performing AI-driven solutions
Evaluate emerging AI technologies, frameworks, and engineering practices to support innovation and align with business and technology strategy
Implement AI reliability, monitoring, observability, security, and governance best practices, including guardrails and human-in-the-loop workflows
Create and maintain technical documentation for software solutions, AI workflows, APIs, system architecture, and operational processes
Mentor team members through technical guidance, code reviews, knowledge sharing, and adoption of AI engineering best practices
Support continuous improvement initiatives, operational excellence, and other engineering projects as assigned by business leadership