Forward Deployment Engineer

T-Mobile•Bellevue, WA

7h•Onsite

About The Position

At T-Mobile, we invest in YOU! Our Total Rewards Package ensures that employees get the same big love we give our customers. All team members receive a competitive base salary and compensation package - this is Total Rewards. Employees enjoy multiple wealth-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year-round money coaches. That’s how we’re UNSTOPPABLE for our employees! Are you ready to join the Un-carrier movement? The Engineer, Forward Deployment is a hands-on technical practitioner who embeds within a T-Mobile business unit to design, build, and iterate on net-new agentic AI solutions. These solutions are developed against requirements defined and scoped by TPMs and business unit stakeholders. Forward Deployment Engineers (FDEs) write code, run experiments, debug production systems, and work closely with business unit stakeholder teams to deliver AI solutions that achieve stated business outcomes. FDEs typically work alongside a Senior FDE who owns the domain relationship and sets technical direction. The Engineer is responsible for the execution layer: building system integrations, tuning prompts, running A/B cohorts, and delivering against validated ROI targets for the domain. Performance is measured by contribution to validated return on investment outcomes for each domain assignment — not by activity or ticket volume. This role reports to the Manager or Senior Manager, Forward Deployment Engineer within the assigned pod. 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.
2-4+ years hands-on software or ML engineering experience in production environments.
Demonstrated ability to write clean, maintainable Python code and to debug and extend existing codebases.
Experience deploying to cloud environments and building system integrations with enterprise data sources.
Python proficiency in production settings: clean, maintainable code; able to read, debug, and extend existing agentic system code written by others.
Working knowledge of LLM integration patterns: prompt engineering, RAG basics, function calling; experience with at least one major LLM provider.
SQL proficiency; experience building system integrations connecting AI solutions to enterprise data sources (CRM, ticketing, ERP, billing); able to build lightweight ETL/ELT pipelines and instrument systems with basic logging.
Comfortable deploying to AWS or Azure, using Docker, and reading CI/CD pipelines.
Ships iterative improvements in days, not weeks; able to communicate technical trade-offs clearly to non-technical partners.
Solid understanding of data privacy requirements in enterprise environments including PII and customer data protection.
Applies responsible AI principles and appropriate data handling practices when building system integrations and AI solutions.
Demonstrated ability to embed within a business unit, earn stakeholder trust rapidly, and deliver working AI systems under real operational constraints, not in sandbox or lab environments.
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.
Experience compressing the gap between proof-of-concept and production-grade delivery.
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

4-7+ years prior experience in a customer-facing, operations, or embedded technical role (care, retail, finance, supply chain).
Exposure to enterprise AI platforms such as Salesforce Einstein, ServiceNow AI, or Microsoft Copilot.

Responsibilities

Implement, configure, and iteratively tune net-new agentic AI solutions deployed in the assigned business domain, working against requirements defined and delivered by TPMs.
Adapt solution behavior, prompt layers, and escalation logic based on live operational feedback on a weekly cycle.
Run controlled A/B experiments in live production cohorts and document measured impact on validated return on investment targets for the domain.
Feed results back to the FDE team and practice leadership with structured analysis to inform delivery prioritization.
Work directly alongside BU teams in the assigned domain across care, retail, finance, supply chain, or other operational contexts.
Observe real workflows, identify friction that AI solutions can address, and conduct structured feedback sessions with BU managers and end users.
Build system integrations connecting AI solutions to BU data sources such as CRM, billing, ticketing, and ERP platforms.
Prepare and normalize business-unit-specific data for AI system consumption.
Build lightweight ETL pipelines feeding operational data into evaluation loops and monitoring dashboards.
Identify and escalate data quality gaps with specific documentation.
Contribute to reporting cycles that support the TPM-defined requirements pipeline.
Document system performance, failure modes, and gaps in a structured format that supports domain planning and delivery prioritization.
Participate in practice reviews to share domain learnings.
Contributes structured documentation: failure modes, edge cases, performance gaps, and develops them into practice reporting cycles.
Participates in rotation planning and knowledge transfer sessions.
Contributes to peer knowledge sharing
Supports onboarding of rotating FDEs at the IC level.
Implements integrations and data normalization pipelines for assigned BU data sources.
Flags data quality issues and raises to Sr. FDE or TPM.
Builds and iterates on assigned components of agentic systems, individual agents, RAG pipelines, prompt layers, while under architectural direction.
Delivers A/B experiments and documents results.