About the role
Why project44? 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. Better for our customers. Better for their business. Better for the world. With our Decision Intelligence Platform, Movement , we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward. Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose. AI at project44 We expect every project44 team member, regardless of role or function, to actively leverage AI in their day-to-day work. Whether you're building product, serving customers, managing people, or running operations, AI is a tool you're expected to use with intent, curiosity, and judgment. We don't expect everyone to be a data scientist. We do expect everyone to be an intelligent user of AI: able to identify where it adds value, direct it effectively, evaluate outputs critically, and govern it responsibly. We invest in our team's AI fluency because we believe it's a competitive advantage for every person at project44, not just our engineers. If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk. In-office Commitment: Our office is where ideas spark, connections thrive, and innovation comes alive. We are looking for candidates who are enthusiastic and committed to joining our team on-site, in our beautiful headquarters four days a week. Together, we’re building something extraordinarily learn, grow, and thrive in our fast-paced, transformative environment. The opportunity Autopilot is project44’s no-code platform for deploying purpose-built workflows with AI agents into the mission-critical workflows supply chain teams run every day — calling carriers, collecting missing data, reconciling documents, resolving exceptions, and running mini-bids within guardrails. It is the product layer that sits on top of our agent portfolio (Freight Procurement, Disruption Management, Network Operations, Execution Recovery, Stockout Risk, and more) and gives customers the steering wheel: configurable triggers, transparent logic, audit history, and human checkpoints at every critical step. This role owns three connected surfaces: 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. The arc you’ll drive: from support, to augment, to automate. The first generation of AI workflows supported users — surfacing the right signal at the right moment. The current generation augments them — taking a discrete action inside a human-run workflow (make the call, collect the milestone, draft the response). The mandate for this role is to push to the next stage: multi-agent workflows that automate complete work tasks end to end — coordinating several agents across a full job (e.g., detect → reach out → confirm → reconcile → write back → close) so an entire operational task runs without a human in the loop, while staying transparent, auditable, and reversible. You’ll own that progression use case by use case, deciding when a workflow is ready to move from augmenting a person to automating the task outright. Natural-language workflow authoring with Mo. Mo is project44’s AI Supply Chain Analyst — a conversational agent embedded in Movement that lets users ask questions of their own data in natural language (NL→SQL / NL→API), grounded in their shipments, business rules, and carrier history. Mo’s roadmap runs from search to analyze to act , and Autopilot is the “act.” A core part of this role is making Autopilot workflows authorable and executable through Mo in plain language — “set up a workflow that calls the carrier whenever an FTL shipment is running two hours late and update the ETA” — and surfacing Autopilot task history and run status back inside Mo’s answers. You’ll own the Autopilot side of that integration and partner closely with the Mo product management team on the shared experience, the trigger/condition/action vocabulary Mo maps natural language onto, and the guardrails for letting a conversational agent stand up an autonomous workflow. We’re moving fast — from shipping roughly one workflow a week to one a day — and the bar is high: very little vibe-coded software is production-ready, and customers only adopt agents they trust. You’ll set the throttle, use case by use case, so speed never outruns trust. What You’ll Do: Lead with customers and research 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. 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. Drive AI innovation 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. Design for trust: configurable controls, transparent logic, audit trails, intervention points, hallucination guards, and throttles tuned per use case. Decide where humans stay in the loop and where agents can act autonomously. Partner with engineering, applied AI, and design on workflow architecture — triggers, conditions, actions, contact-resolution strategy, retry and cadence logic, and closure semantics — and on the tooling that lets us scale workflow production toward one per day. Stay ahead of a fast-moving competitive field of agentic logistics startups; know precisely why project44’s network and context are the durable advantage and build the product to exploit it. Write outcomes-based
