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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Cargomatic, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $150,000.0 - $210,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Code Review, DevOps, Github, PostgreSQL, MongoDB, Open Source Technology, Regression Testing, OpenAI, Software Engineering, SQL Databases, Systems Integration, TypeScript, Workflow Management Systems, ReactJS, Large Language Models, Zapier, Agentic-AI, Uipath, Low-code, Graphql, Restful APIs, Intelligent Document Processing, Human in the Loop, Amazon Redshift - **Published:** October 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ai-engineer--fd796fae-f4cc-42f8-904c-b2d514f4adc5 ## About the Role Artificial Intelligence (AI), Fax Machines, Trucking, Revenue Growth, Artificial Intelligence (AI), Automation, Establish Priorities, REST (Representational State Transfer), Application Programming Interface (API), GraphQL, 5+ years of software engineering, including hands-on experience shipping LLM-based or automation systems to production. - Hands-on experience with LLM APIs (Anthropic, OpenAI) and agentic patterns: tool calling, structured outputs, exception routing, and human-in-the-loop. Sound judgment on when to use LLMs, deterministic software, or RPA. - Experience building evaluation for non-deterministic systems: golden datasets, LLM-as-judge calibrated against human labels, and regression testing when prompts or models change. - Experience evaluating and integrating third-party AI or automation tools, weighing cost, effort, and risk against building in-house. - Strong backend engineering and REST/GraphQL API integration on AWS (Node.js/TypeScript preferred). - Clear communication with non-technical stakeholders: you can turn an operational workflow into a system design and explain tradeoffs to leadership. - A track record of shipping working systems beyond demos or prototypes. Nice to Have - Freight or logistics background (TMS, EDI, drayage, or carrier/shipper operations). - Document AI or OCR experience, including confidence calibration and human review workflows. - Experience with RPA platforms (UiPath preferred) and with modernizing or augmenting existing bots with AI. - Low-code automation (Zapier) and workflow orchestration, with judgment on when a workflow should move to code. - SQL and dbt; experience with Redshift, MongoDB, PostgreSQL, or Firestore. - React for lightweight internal tools. - Active GitHub or open-source work. ## Description 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. Scope This role consumes platform capabilities and does not own core platform, authentication, or infrastructure. Agent infrastructure needs, such as tool authorization and observability, are evaluated for buy or partner options first, in partnership with Engineering and DevOps. Initial document AI work uses managed services; model hosting and training are in scope only if evaluation shows a clear need. All production work follows Engineering's standards: code in our shared repositories, Engineering code review and architecture review, standard deployment and monitoring, and Engineering sign-off on production readiness. Every automation is documented with an owner, its API dependencies, and a runbook, so any engineer can operate and inherit what you build.