Sr Forward Deployment Engineer

T-Mobile•Frisco, TX

1d•$142,300 - $256,700•Onsite

About The Position

The Senior Engineer, Forward Deployment is an experienced technical practitioner who embeds within a T-Mobile business unit and operates with significantly greater autonomy than an Engineer FDE. Senior FDEs are the primary execution force: they drive the technical agenda for net-new agentic AI delivery, mentor Engineers co-deployed alongside them, and step in as acting domain lead when needed. This role bridges deep hands-on engineering with emerging leadership accountability. Performance is measured by validated return on investment outcomes for the domain and by the quality and impact of the delivery team supported. This role reports to the Manager or Senior Manager, Forward Deployment Engineer within the assigned domain. We pride ourselves on fostering a culture of innovation, advocating for agile methodologies, and promoting transparency in all that we do. Join us in embodying the spirit of the 'Un-carrier' and make a tangible impact! If you're up to the challenge, apply today!

Requirements

Bachelor's Degree Computer Science, Software Engineering, Data Science, or related technical field. Relevant equivalent experience accepted.
4-7+ years production engineering experience with demonstrated hands-on LLM deployment: prompt engineering, RAG architecture, agent orchestration.
Validated ability to operate autonomously in ambiguous environments and deliver measurable AI outcomes.
Experience building system integrations with enterprise data sources.
Python Engineering: Strong production Python; proficiency in at least one additional language (Go, TypeScript, or Java). Architects robust agentic system components and writes code others can extend.
LLM System Design: Demonstrated hands-on experience deploying LLM-based systems: prompt engineering, RAG architecture, agent orchestration, hallucination mitigation, and performance benchmarking.
Data Engineering & Integration: Proficiency in complex SQL, ETL/ELT pipeline design, and experience building system integrations connecting AI solutions to enterprise and telecom data sources.
Cloud-Native Deployment: Docker, Kubernetes, CI/CD, and infrastructure experience on AWS (primary) or Azure/GCP; able to instrument deployed systems with real-time logging and feedback collection.
Technical Leadership Without Authority: Proven ability to lead technical delivery and mentor engineers in ambiguous environments; adapts communication to BU partners and leadership alike.
Responsible AI & Data Security: Applies data privacy, security, and responsible AI principles in all system design and deployment decisions. Builds guardrails and compliance considerations into agentic solutions. Understands T-Mobile's data classification standards and handles sensitive data appropriately.
Forward Deployment Mentality: Demonstrated ability to embed within a business unit, earn partner trust rapidly, and deliver working AI systems under real operational constraints, not in sandbox or lab environments.
Comfortable operating in ambiguity without heavy process scaffolding.
Stakeholder Translation: Ability to move fluidly between technical implementation and business communication, converting engineering tradeoffs into plain-language impact narratives for BU managers, and converting vague business asks into precise engineering requirements.
Technical Leadership: Proven ability to lead technical delivery and mentor engineers in ambiguous environments; adapts communication to BU stakeholders and leadership alike.
Rapid Prototyping & Production Readiness: Experience compressing the gap between proof-of-concept and production-grade delivery.
Knows when a prototype is good enough to ship and how to harden it without full re-architecture.
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

7-10+ years prior experience in solutions engineering, embedded technical, or forward deployment role.
Track record of delivering AI solutions that measurably improved a business outcome metric.
Experience navigating stakeholder environments from BU operators to middle management.

Responsibilities

Drive the technical agenda for net-new agentic AI delivery within the assigned business domain.
Lead scoping and diagnostic sessions with BU stakeholders and TPMs to define requirements, map workflows, and assess data and integration readiness.
Maintain trusted working relationships with BU managers and communicate delivery progress and milestones to domain stakeholders.
Design and deliver net-new agentic AI solutions in live production environments.
Implement system integrations, RAG pipelines, prompt orchestration layers, and multi-agent workflows tailored to BU-specific needs.
Run controlled A/B experiments and document measured return on investment impact.
Build integration layers connecting AI solutions to BU tools and data sources.
Normalize BU-specific datasets for AI system consumption.
Identify and close data quality gaps and feed domain-specific insights back to the practice through structured engineering handoffs.
Mentor and technically guide Engineers co-deployed in the same domain.
Provide code review, solution iteration feedback, and coaching on delivery standards.
Support onboarding of new FDEs rotating into the domain and contribute to FDE practice methodology.
Contribute to practice reporting cycles that support TPM-defined requirements and delivery planning.
Document failure modes, edge cases, and performance gaps in a structured format.
Participate in rotation planning and knowledge transfer sessions.
Owns and leads scoping sessions independently.
Drives the technical agenda for the full domain.
Communicates directly with BU managers and middle management on delivery progress and milestone accountability.
Owns end-to-end solution architecture and delivery.
Makes independent design decisions across the full agentic stack.
Defines the framework and is accountable for measured return on investment outcomes.
Designs the integration architecture.
Defines reusable patterns for the domain.
Leads the structured engineering handoff to the practice and makes final calls on data quality remediation approach.
Owns domain-level reporting narrative.
Synthesizes Engineer inputs into practice-level insights.
Authors domain case studies and contributes to FDE playbook development.