Forward Deployed Engineer

Agents have changed the game for software delivery and efficacy. Diligent is the leading GRC platform in the world, and we are racing ahead to take the agents show on the road and work with the customers where they work. The FDE function will lead the change on how we embed AI agents into some of the world’s most complex governance, risk and compliance environments. This is not a support or consultancy role. It is a builder role, for someone who is equally comfortable reading a failing agent trace, running a discovery workshop with a bank’s internal audit team, and translating what they find into a production-grade agentic solution. You will be building AI agents for GRC professionals, not assistants that surface suggestions, but agents that own complex, multi-step workflows end to end. Agents that customers can hand a task to and trust it will come back done. Closing the gap between a promising prototype and something a company Board and ELT depends on is a completely. Here’s a breakdown of what you’ll do Embed directly with major enterprise customers (global banks, regulated corporates) across EU and US; sitting with internal audit teams, risk functions, compliance and governance professionals to understand their real workflows and devise agentic solutions to intelligently automate them creating tremendous efficacy and efficiencies for our customers. Run agent-focused dis [... source excerpt omitted ...] with practitioners; distinguishing between workflows that need an agent and those that need a button. Source, integrate, and move data between enterprise systems as part of live customer implementations — understanding the real data landscape customers operate in and building reliable pipelines to support it. Take agents from prototype through to production-grade reliability: building evaluation infrastructure, golden datasets, guardrails, and observability so a compliance team can trust the output. Master the hard failure modes of agentic AI — silent regressions on model updates, context window degradation, prompt instability, non-deterministic outputs — and build the infras [... source excerpt omitted ...] ss multiple enterprise accounts to identify which agent behaviours should be generalised into the platform, feeding field insights back to product and engineering. Translate what customers actually need into concrete API surfaces, data integration requirements, and agent tool specifications for internal teams. These are the essentials you’ll need to get an interview Hands-on experience shipping at least one SaaS production agent from prototype to evaluation to live deployment to regression — and the scars to prove it. Proven implementation experience: you have worked on enterprise deployments where you have sourced data from multiple systems, built integrations, and onboarded