AI Product Lead

The Company Serving the People Who Serve the People Nimbus Data Systems is driven by the excitement of building, implementing, and maintaining technology that is transforming the Govtech industry by bringing governments and its constituents together. We are on a mission to support our customers with meeting the needs of their communities and implementing our technology in ways that are equitable and inclusive. Nimbus Data Systems has consistently appeared on the GovTech 100 list over the past 5 years and has been recognized as the best companies to work on BuiltIn. Over the last 25 years, we have served 5,500 federal, state, and local government agencies and more than 300 million citizen subscribers power an unmatched Subscriber Network that use our digital solutions to make the world a better place. With comprehensive cloud-based solutions for communications, government website design, meeting and agenda management software, records management, and digital services, Nimbus Data Systems empowers stronger relationships between government and residents across the U.S., U.K., Australia, New Zealand, and Canada. By simplifying interactions with residents, while disseminating critical information, Nimbus Data Systems brings governments closer to the people they serve—driving meaningful change for communities around the globe. Want to know more? See more of what we do here. Job Summary Nimbus Data Systems serves more than 7,000 public-sector agencies and powers approximately 30 billion digital interactions annually. Within the Office of the CTO, we operate an AI-native software development lifecycle: a production engineering model in which autonomous agents perform high-confidence implementation work and senior engineers orchestrate, review, and own the outcome. The model is established and operating; we are scaling it across additional delivery teams. This role joins one of those teams. Our engineering standards are calibrated to the practices used by leading software and AI organizations — generator-verifier architecture , eval-driven development , staged deployment , and high-volume autonomous pull-request pipelines — operated within a FedRAMP-authorized environment. We are hiring practitioners who will set and uphold these standards, not engineers who simply use AI tooling. Operating constraints (non-negotiable): agents execute only within branches; all agent-generated code passes senior human review before merging to production; autonomous execution is a force multiplier and does not transfer accountability away from the responsible engineer. Compliance obligations — NIST 800-53 Rev 5, WCAG, SOC 2, and applicable FedRAMP authorizations — are treated as engineering requirements and a source of competitive advantage. Conventional delivery distributes product management, design, and engineering leadership across separate individuals, introducing coordination overhead at each handoff. In an AI-native lifecycle, where implementation throughput is high, those handoffs become the primary constraint. This role consolidates the product, design, and delivery-leadership disciplines into a single accountable owner who synthesizes them rather than displacing the specialists who practice them. The role translates product intent into shipped, compliant software by directing the agent array and implementing front-end experiences directly. It combines the responsibilities of a staff product engineer, a design engineer, and an engineering manager, and requires demonstrated capability across all three. What Your Impact Will Look Like Own the product outcome of the team end to end — from problem definition through accepted, in-production software. You hold the roadmap commitment and the quality bar. Direct the agent array across three execution modes — Interactive (real-time pairing with an agent), Supervised (agents propose changes; you review and gate each pull request), and Fully Autonomous (agents execute a scoped workstream end to end; you review the output). You determine the appropriate mode for each workstream based on risk and measured agent reliability. Implement production front-end code from the same repository as the engineering team, under engineering review. You deliver the user experience directly rather than specifying it for others to build. Apply product and design rigor at every stage — discovery, user research, information architecture, interaction design, and prioritization — and ensure that increased build velocity does not compromise upstream rigor. Author the prompts, evaluation criteria, and acceptance criteria that convert product intent into work an agent can execute and a verifier can validate. Enforce the human review gate and accessibility standards in partnership with the lead engineer, ensuring no agent-generated code reaches production without senior human review and that WCAG conformance and compliance requirements are designed in from the outset. Set and maintain the team’s delivery cadence and contribute improvements to the shared operating mode