Job Opportunities API

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 wi th 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 discovery workshops, rapidly prototype agentic solutions, and test them 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 infrastructure that prevents them.   Synthe iz e   le arni ng  across  multiple enterprise accounts t o  ide ntify  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 complex customers onto technical platforms. This is a hard requirement.   Deep understanding of the Agent Development Life Cycle: evaluation frameworks, guardrails, observability, prompt versioning, and golden datasets.   Strong  software engineering fundamentals: APIs, data pipelines, backend services, agent tool-calling frameworks (MCP or equivalent). TypeScript and/or Python.   Experience with multi-agent orchestration patterns: orchestrator/sub-agent architectures, agent-to-agent coordination, shared context models. &n