Forward Deployment Engineer - Devin/Claude AI with Java Experience
- Employment
- Contract
- Location
- Charlotte, United States
- Posted
- 5d ago
Job Description: The Forward Deployment Engineers (FDEs) will act as hands-on full-stack engineers responsible for enabling and scaling GenAI tools (Devin, Claude Code, Cursor). The Role Responsibilities: Lead end-to-end rollout of Devin, Claude Code, and Cursor across CDXO and LOB engineering teams Act as hands-on full-stack developers, building and integrating solutions into enterprise SDLC workflows (CI/CD, repos, APIs) Define and execute enterprise rollout strategies, including phased onboarding and scaling models Partner closely with client teams to improve the overall product delivery lifecycle through effective and optimized utilization of GenAI tools Conduct developer onboarding, training, and enablement sessions, driving best practices for prompt engineering and workflow integration Monitor tool usage, performance, and efficiency metrics, and continuously optimize adoption and outcomes Provide hands-on troubleshooting, performance tuning, and scaling support for engineering teams Establish and promote best practices and reusable playbooks for enterprise-wide GenAI adoption Ensure alignment with client governance, security, and compliance requirements. Requirements: Successful deployment and scaling of Devin, Claude Code, and Cursor across CDXO and LOB teams Demonstrable improvement in the product delivery lifecycle, including faster development, testing, and remediation cycles through effective GenAI utilization Increased developer productivity and automation outcomes driven by optimized usage of Devin, Claude Code, and Cursor Defined, adopted, and standardized enterprise playbooks and best practices for GenAI usage across teams Continuous monitoring and reporting of adoption metrics, usage efficiency, and value realization Stable, secure, and governed integration of GenAI tools into the enterprise SDLC ecosystem Proactive identification and resolution of adoption gaps, inefficiencies, and scaling challenges