What are the responsibilities and job description for the Forward Deployment Engineer - Devin/Claude AI with Java Experience position at Delan Associates, Inc?
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