The opportunity
At project44, we believe in better. We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential.
What you'll do
AI Workflows: the growing library of packaged, customer-deployable agent workflows (e.g., validate early ETAs, late-shipment carrier outreach, collect missing milestones, stale-position investigation). You define which jobs we automate next, in what sequence, and to what standard.
AI Agent Workflow Manager (fka Autopilot): the no-code configurator itself: the trigger / condition / action canvas, workflow variants, multi-agent orchestration, human-in-the-loop controls, and the build-and-deploy experience that lets customers (and our own teams) ship workflows without engineering.
AI Agent Analytics & Reporting: the measurement layer (AI Agent Analytics, LunaIntel and LunaVoice dashboards, collaboration and carrier-performance reporting) that proves outcomes by use case and persona, exposes agent performance to customers, and closes the loop back into the roadmap.
Own the customer problem before the solution. Every workflow starts from a: clearly stated customer problem, who is impacted (planners, logistics managers, appointment and yard managers, carrier dispatch, drivers), and when it occurs — not from a feature idea.
Run primary research continuously: customer interviews, ride-alongs with operations teams, design-partner pilots, Customer Advisory Board (CAB) validation sessions, win/loss and churn intake reviews, and direct analysis of platform behavior.
Recruit and manage design partners for shadow-mode pilots: where the agent logs what it would do before it acts — to establish honest baselines and earn trust ahead of live deployment.
What they're looking for
- Be the domain and product expert in customer-facing settings: demos, executive briefings, CAB, and conferences. Translate what you hear into a prioritized, defensible roadmap.
- Push the frontier of what agents can safely do in production: autonomous voice and email outreach, document parsing and reconciliation, reason-code classification and write-back, and multi-agent workflows that coordinate several agents across a single business outcome.
- Move workflows up the maturity curve: from supporting a user (surfacing a signal), to augmenting them (taking one action in a human-run flow), to automating a complete work task (a multi-agent workflow that runs the whole job end to end). Define, for each use case, the bar a workflow must clear to graduate to the next stage.
- Make workflows authorable and executable in natural language through Mo,: project44’s AI Supply Chain Analyst — so a user can describe a workflow conversationally and have Autopilot stand it up, run it, and report back. Own the Autopilot side of the integration and the mapping from natural language to triggers, conditions, and actions, partnering with the Mo product management team.
