AI Product Lead

Granicus•Remote,

2d•$144,000 - $184,000•Remote

About The Position

Granicus operates an AI-native software development lifecycle within the Office of the CTO, utilizing a production engineering model where autonomous agents perform high-confidence implementation work, and senior engineers orchestrate, review, and own the outcome. This role is part of a team scaling this established model across additional delivery teams. The engineering standards are aligned with leading software and AI organizations, including generator-verifier architecture, eval-driven development, staged deployment, and high-volume autonomous pull-request pipelines, all within a FedRAMP-authorized environment. The role requires practitioners who will set and uphold these standards, not just engineers who use AI tooling. Key operating constraints include agents executing only within branches, all agent-generated code passing senior human review before merging to production, and autonomous execution serving as a force multiplier without transferring accountability from the responsible engineer. Compliance obligations such as NIST 800-53 Rev 5, WCAG, SOC 2, and applicable FedRAMP authorizations are treated as engineering requirements. This role consolidates product management, design, and delivery leadership into a single accountable owner, synthesizing these disciplines rather than displacing specialists. The role translates product intent into shipped, compliant software by directing the agent array and implementing front-end experiences directly, combining responsibilities of a staff product engineer, a design engineer, and an engineering manager.

Requirements

End-to-end product delivery as a builder. You have owned a product surface end to end — making product decisions, designing the experience, and personally writing front-end code deployed to production users.
Production front-end engineering. Proficiency in modern JavaScript/TypeScript and React (or an equivalent framework), strong CSS, and the ability to move fluently between design files and application code.
Design discipline. Demonstrated competence in information architecture, interaction design, and accessibility (WCAG), with the judgment to evaluate experience quality, not only functional completeness.
Direction of AI coding agents. Practical experience using coding agents on production work, including a working understanding of their failure modes and the controls required to use them as leverage without compromising quality or review.
A track record of defining how teams work. You have established a process, pattern, or standard that other practitioners adopted.
High autonomy under ambiguity. You produce structure and direction where none is provided.

Nice To Haves

High-assurance or regulated delivery experience. Shipping within FedRAMP, FedRAMP High, defense, financial services, healthcare, or another NIST 800-53 / SOC 2 / HIPAA-bound environment, treating those constraints as design inputs.
Public-sector or govtech product experience, including an understanding of constituent- and caseworker-facing use cases.
Design-systems ownership. Experience building or maintaining a component library used across multiple teams.
Experience operating within an eval-driven or test-first engineering culture at a leading software organization.

Responsibilities

Own the product outcome of the team end to end — from problem definition through accepted, in-production software.
Hold the roadmap commitment and the quality bar.
Direct the agent array across three execution modes — Interactive, Supervised, and Fully Autonomous.
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.
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.
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.
Ensure no agent-generated code reaches production without senior human review.
Ensure WCAG conformance and compliance requirements are designed in from the outset.
Set and maintain the team’s delivery cadence.
Contribute improvements to the shared operating model that other delivery teams adopt.