Engineer,AI (Hyderabad)

About TMUS Global Solutions T-Mobile is Americas supercharged Un-carrier, challenging conventions and setting current standards in wireless. With the nations largest and fastest 5G network, T-Mobile delivers advanced connectivity and unmatched value to millions across the U.S. Were unwaveringly obsessed with providing the best possible service experience, driven by a spirit of disruption that fuels competition and innovation in wireless and beyond. Job Description What You'll Do: - Design and develop agent orchestration frameworks and execution engines supporting complex AI-driven workflows. - Design and optimize low-latency services supporting real-time conversational and voice interactions. - Build reusable platform services, SDKs, APIs, and libraries that accelerate development of conversational AI applications. - Design and implement Model Context Protocol (MCP) integrations and frameworks that enable secure, scalable access to enterprise tools, APIs, data sources, and agent capabilities. - Develop reusable tool-calling, context-sharing, and interoperability services that support MCP-enabled agent ecosystems. - Develop runtime services for text- and voice-based agent interactions, including context management, tool execution, memory, and state orchestration. - Architect microservices and event-driven systems that integrate AI reasoning with enterprise systems such as customer, billing, network, and support platforms. - Design mechanisms that safely translate model outputs into deterministic business actions and workflows. - Collaborate with AI/ML engineers to integrate foundation models, retrieval systems, evaluation frameworks, and agent capabilities into production platforms. - Drive platform reliability, scalability, observability, and performance across high-volume AI workloads. - Establish software architecture standards, engineering best practices, and reusable design patterns for agentic systems. - Contribute to technical strategy and platform evolution by evaluating emerging AI, orchestration, and distributed systems technologies. - Mentor engineers and promote engineering excellence through design reviews, documentation, and knowledge sharing. What You'll Bring: - 7+ years of software engineering experience building distributed systems and cloud-native applications. - Strong proficiency in Python or Java, with working knowledge of both preferred. - Experience designing APIs, microservices, workflow engines, or platform services. - Experience building systems that integrate with LLMs, AI services, or conversational platforms. - Experience designing highly available distributed systems and service-oriented architectures. - Strong understanding of software architecture, system design, scalability, resiliency, and performance optimization. - Demonstrated experience leading technical design discussions, driving architectural decisions, and influencing engineering direction across teams. - Experience with cloud-native technologies, including containers, Kubernetes, and service-based architectures. - Excellent problem-solving, debugging, and technical communication skills. - Bachelors degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience. - Experience integrating AI systems with enterprise applications, APIs, tools, or data platforms through standardized integration patterns. Must Have Skills: - Python software engineering - Distributed systems, APIs, and microservices - LLM, AI service, and conversational platform integration - Cloud-native architecture, containers, and Kubernetes - Software architecture and technical leadership Nice-to-Have: - Experience building agent orchestration frameworks, workflow platforms, or AI execution systems. - Experience with conversational AI, voice platforms, speech technologies, or real-time interaction systems. - Familiarity with retrieval systems, vector databases, memory architectures, and evaluation frameworks. - Experience designing internal developer platforms, SDKs, or reusable engineering frameworks. - Knowledge of event-driven architectures, distributed workflows, and enterprise integration patterns. - Experience with Model Context Protocol (MCP), agent tool-calling frameworks, or similar standards for connecting AI systems with external tools and services. - Experience designing reusable tool integration frameworks, context management systems, or agent interoperability platforms. .