AI Engineer

AI Engineer – Agentic Automation
Location: Remote
Compensation: $150,000 – $210,000

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.

Cargomatic is seeking a highly hands-on AI Engineer who thrives in fast-paced environments and is passionate about rapid prototyping and deploying standalone AI-driven applications. This role focuses on building agentic AI systems and automation tools that integrate with our ecosystem and deliver immediate business value — working alongside a live TMS and RPA stack, with real exception-handling and audit requirements. You will work at the intersection of AI, product development, and infrastructure — quickly turning ideas into working prototypes and scaling the most promising solutions into production.

Key Responsibilities
- Design agentic AI workflows for structured logistics operations, with clear escalation paths to human review or RPA where deterministic handling is required
- Call core platform/internal APIs to integrate automation workflows into our TMS and other internal systems, partnering with front-end and backend engineering on execution
- Integrate LLMs, APIs, and data pipelines into reliable production services — including data engineering tie-ins with systems like Firestore and Redshift
- Build and implement agentic AI systems capable of autonomous decision-making and task execution
- Own evaluation frameworks — accuracy, latency, cost per call, fallback behavior, and exception handling
- Implement guardrails, monitoring, and audit trails for production AI systems
- Collaborate with Product and Ops to define success metrics before building
- Leverage modern LLM ecosystems, including Claude-based and code-first AI frameworks, to build intelligent agents and workflows
- Stay current with advancements in AI/ML, especially in agent frameworks and automation tooling
- Build lightweight frontend interfaces from product prototypes, and partner with full-stack engineering to productionize and scale successful prototypes

Required
- 3+ years in software engineering with hands-on experience shipping AI or automation systems in production enterprise environments - Strong grasp of agentic AI concepts — tool use, function calling, exception routing, and confidence handling at scale - Clear understanding of where LLMs and agentic systems outperform RPA — and where they don't - Comfortable working alongside RPA developers — understands how to hand off structured outputs into downstream deterministic workflows - Experience with LLM APIs (Claude, OpenAI) including prompt design, orchestration, and evaluation - Workflow orchestration experience — n8n, LangChain, Temporal, or similar - Familiarity with no-code/low-code automation tools including Zapier, with the ability to know when to move beyond them - Experience building rules-based automation with structured inputs, exception handling, and audit trails - Familiarity with RAG and vector databases - Experience integrating with TMS or ERP platforms, including calling core platform/internal APIs to execute production actions - Backend proficiency in Node.js; comfort with React for building lightweight interfaces; comfortable partnering with full-stack and data engineering teams to ship into production systems - AWS experience (Lambda, ECS, S3) - Familiarity with databases and data warehouses such as MongoDB, RDS (PostgreSQL/MySQL), Firestore, and Redshift - Experience integrating third-party APIs and building microservices - Active GitHub or demonstrated track record of shipping AI systems in production beyond the prototype stage