Manager, Software Development [T500-28886]

TMUS Global Solutions; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

TMUS Global Solutions seeks a seasoned Manager, Software Engineering – AI Platform Engineering to lead offshore AI platform teams delivering agentic AI and cloud-native services. You will own delivery decisions, align roadmaps with onshore priorities, and drive reliability and cost discipline across production AI services.

You will coach engineers, manage hiring and performance, and ensure high-quality software through best practices, automated testing, and observability.

Qualifications

• Bachelor’s degree or equivalent practical experience in CS or related field.
• 10+ years software/platform engineering experience; 3+ years AI platform engineering.
• 5+ years leading engineering teams with hiring and performance management.
• Strong depth in AI platforms, distributed systems, and production-grade software.
• Experience with cloud-native tech, Kubernetes, CI/CD, and observability tools.

Responsibilities

• Lead distributed AI platform engineering teams across time zones.
• Drive predictable delivery, reliability, and incident response for AI services.
• Partner with architects, product managers, and onshore teams to execute roadmaps.
• Ensure security, governance, and cost optimization of cloud resources.
• Mentor engineers, oversee performance reviews, and foster culture of excellence.
• Communicate progress, risks, and priorities to stakeholders.

Skills

Distributed systems

Microservices

REST APIs

gRPC

Cloud-native

AI platforms

Agentic AI

LLM-powered services

Team leadership

Effective communication

Education

Bachelor's degree in Computer Science or related

Tools

Kubernetes

CI/CD pipelines

Infrastructure as Code

Terraform

Helm

Datadog

OpenTelemetry

Docker

GitHub Actions

Job description

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.

TMUS Global Solutions:

TMUS Global Solutionsis 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.

About the Role:

• The Manager, Software Engineering – AI Platform Engineering leads T-Mobile's offshore engineering team responsible for delivering AI Platform & Agent Systems Engineering and Platform & Reliability Engineering capabilities. This team includes AI Platform Engineers, Platform & Reliability Engineers, and a Senior Product Owner working closely with onshore engineering, architecture, product, AI/ML, and platform teams.
• This is a hands-on engineering leadership role responsible for engineering execution, production reliability, delivery predictability, talent development, and operational excellence across AI platform services supporting conversational AI, agentic AI, enterprise integrations, and cloud-native platform capabilities.
• The manager partners with onshore engineering leaders to execute platform roadmaps, improve engineering practices, and ensure reliable operation of production AI services. Success is measured through engineering quality, platform reliability, predictable delivery, operational excellence, engineering capability growth, and effective collaboration across globally distributed teams.

What This Role Owns:

Decision rights are explicit so this role is a leader and not a coordinator. The manager owns day-to-day delivery and production decisions for team-owned services within architectural and roadmap guardrails; prioritization execution through the Senior Product Owner; hiring, performance management, and capability development for the engineering team; on-call ownership and incident response for the team's coverage window; and operational tradeoffs involving reliability, feature velocity, platform sustainability, and cost. The manager also serves as the primary distributed team leadership interface with architecture, product, and platform teams.

Key Responsibilities:

• Lead, coach, and develop a distributed software engineering team comprising AI Software Engineers, AI Systems Engineers, and a Senior Product Owner.
• Drive predictable delivery of consumer AI platform capabilities, platform engineering initiatives, and reliability improvements in partnership with onshore engineering and product teams.
• Partner with onshore architects, engineering managers, and product managers to execute roadmap priorities and ensure alignment across distributed teams.
• Support delivery of reusable AI platform capabilities including agent orchestration services, conversational platforms, SDKs, Model Context Protocol (MCP)-enabled integrations, and AI runtime services.
• Foster engineering excellence through software development best practices, code quality, automated testing, CI/CD, and operational discipline.
• Ensure reliability, availability, observability, and operational readiness of production AI platform services.
• Lead hiring, performance management, mentoring, and career development while building a collaborative, high-performing engineering culture.
• Drive incident management, root cause analysis, and continuous improvement initiatives to enhance platform reliability and operational excellence.
• Ensure compliance with security, governance, and software engineering standards while optimizing cloud resources and platform costs.
• Communicate delivery progress, risks, dependencies, and engineering priorities to stakeholders across the organization.

What You'll Bring:

• Bachelor's degree in computer science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
• 10+ years of software engineering or platform engineering experience, with at least 3 years specifically in AI platform engineering, agentic AI systems, or LLM-powered service development.
• 5+ years leading engineering teams, including hiring, performance management, delivery accountability, and talent development.
• Demonstrated technical depth in AI platform or distributed systems engineering — able to evaluate architecture decisions, identify design flaws, drive technical tradeoffs, and hold engineering teams to a high-quality bar without relying on others to translate.
• Experience with cloud-native technologies, including Kubernetes, CI/CD platforms, Infrastructure-as-Code, and modern observability tooling.
• Experience leading distributed or geographically dispersed engineering teams across time zones.
• Experience partnering with Product Owners or Product Managers in Agile delivery environments.
• Strong communication skills and the ability to influence engineering and business stakeholders.
• Experience building software platforms that support consumer-facing AI applications, conversational AI, agentic AI, or LLM-powered services.

Must Have Skills:

• Engineering manager or technical lead with a track record of hiring, coaching, and growing software engineering teams
• Background building or operating AI platforms, ML platforms, or developer platforms that run in production at scale
• Hands‑on experience with agentic AI systems, AI agents, chatbots, virtual assistants, or conversational AI products
• Experience shipping LLM-powered applications or generative AI features into production, including prompt design, model integration, and inference optimization
• Strong foundation in distributed systems, microservices, REST or gRPC APIs, and backend software architecture
• Proficiency with cloud-native technologies — Kubernetes, Docker, CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), Infrastructure-as-Code (Terraform, Helm), and observability tooling (Datadog, Splunk, OpenTelemetry)
• Experience managing distributed, remote, or globally distributed engineering teams across multiple time zones
• Experience with voice AI, speech AI, or real-time telephony systems — including speech-to-text (STT), text-to-speech (TTS), voice bots, IVR, contact center AI, or conversational phone applications

Nice to Have:

• Exposure to Model Context Protocol (MCP), tool‑calling frameworks, function calling, or building reusable AI service components
• Familiarity with AI observability, LLM monitoring, model evaluation, or responsible AI and AI governance practices
• Experience with multimodal AI, vision‑language models, document AI, or real‑time data processing beyond voice
• Background leading cross‑functional teams that include engineers, platform specialists, and product managers or product owners working toward a shared delivery roadmap