AI Engineer

About the role

AI Engineer – Agentic Automation Location: Remote Compensation: $150,000 – $210,000 Reports to: Director of Product

Join a rapidly growing company disrupting the trucking industry! Cargomatic is the #1 technology platform and digital marketplace for powering world-class, local trucking. Take a look around you. Literally everything humans build, grow, or sell has spent time on a truck. Local trucking is the lifeblood of every regional economy, and yet this $82 billion industry still relies heavily on phone calls and fax machines. Cargomatic is transforming the way goods move around every local node in the supply chain by connecting shippers and commercial truck drivers with mobile technology. We are solving complex, real-world problems every day, and giving full transparency to the shipping process.

About the Role

Cargomatic runs a live TMS, an RPA stack, and many manual, exception-heavy logistics workflows across pricing, dispatch, documents, billing, and invoicing. We're looking for a hands-on AI Engineer to automate them. Product identifies and prioritizes the opportunities. You'll determine the right way to automate each one (build, buy, or partner) and deliver it in production, in partnership with our Engineering and Platform teams and following their standards. You'll report to the Director of Product, with dedicated capacity for automation delivery, and work closely with Operations and our RPA team.

What You'll Do

- Build and ship automations and agentic workflows that call our internal REST and GraphQL APIs to read data and take production actions. - Deliver document automation (OCR and extraction) using managed services and LLM vision. This includes confidence thresholds, deterministic validation against TMS data, and routing low-confidence results to human review. - Determine and design the right approach for each workflow: LLM, deterministic software, RPA, or a purchased tool, with clean handoffs between them. - Run build vs. buy evaluations, including POCs and bake-offs covering total cost of ownership, integration effort, and operating burden. Then implement the decision, whether that means building or integrating and rolling out what we buy. - Partner closely with our RPA team (UiPath). Identify existing bots that should be augmented or replaced with AI, and design workflows where AI and RPA hand off cleanly. - Coordinate with Engineering and Platform when automation needs platform changes. Scope the ask clearly, then deliver your side. - Build the evaluation infrastructure for every AI system we ship: labeled datasets, LLM-as-judge pipelines, regression testing, and release gates. Product defines the quality criteria; you make them executable. - Make AI systems dependable through validation, fallbacks, idempotency, permissions, audit trails, monitoring, and capture of reviewer corrections. - Inventory and rationalize our existing low-code automations (Zapier): decide which to keep, harden, consolidate, or replace. Check what our existing stack already covers before adding anything new, and avoid depending on tools we plan to retire. - Prototype quickly against real workflows and data, and take successful work all the way to production.