Website:
talent500.com
Job details:
About T-Mobile:
T-Mobile US, Inc. (NASDAQ: TMUS) is America’s supercharged Un-carrier, powered by an award-winning 5G network that connects more people in more places than ever before. With its unique value proposition of best network, best value, and best experiences, T-Mobile is redefining connectivity, fueling competition, and driving the next wave of innovation in wireless and beyond. Headquartered in Bellevue, Washington, T-Mobile provides services through its subsidiaries and operates its flagship brands, T-Mobile, Metro by T-Mobile, and Mint Mobile.
TMUS Global Solutions:
TMUS Global Solutions is a world-class technology organization accelerating T-Mobile’s global digital transformation. Our teams combine engineering talent, technology expertise, and collaborative ways of working to build secure, scalable solutions that improve customer and employee experiences. We foster innovation, agility, transparency, and strong enterprise partnerships to deliver measurable business outcomes.
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
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