AI Engineer

Our client is a world-class, asset-based transport and logistics provider building cutting-edge automation solutions across North America. They are seeking a highly motivated AI Engineer to build production AI agents and MCP (Model Context Protocol) integrations that automate complex logistics workflows like dispatch, billing, and fleet operations. In this role, you will design and deploy production-grade AI agents and custom MCP servers to automate high-stakes supply chain operations. You will develop hybrid knowledge retrieval pipelines using RAG, KAG, and CAG strategies to route between static reference data and dynamic operational queries. You'll stay hands-on implementing LLM integrations, function calling, and prompt workflows, deploying agent infrastructure on Kubernetes using GitOps practices while mentoring junior team members.

Why This Role

You'll work on high-stakes, real-world logistics automation, writing Python code to orchestrate autonomous AI agents rather than just building notebooks or prototypes.

Client's Tech Stack

Python, OpenAI/Anthropic APIs, Kubernetes, SQL, big data systems, and agent orchestration frameworks.

What You'll Do:

• Design and deploy custom MCP servers and multi-agent workflows for fleet automation.
• Develop hybrid knowledge retrieval engines (RAG, KAG, and CAG) to support complex logic.
• Deploy agent microservices on Kubernetes using GitOps and establish comprehensive observability.

What You'll Bring:

• 3+ years of experience building and deploying production-grade AI applications in Python.
• Hands-on proficiency with LLM integration patterns: function calling, tool use, and structured outputs.
• Solid understanding of databases (SQL/NoSQL) and knowledge graphs or data warehousing.
• Excellent communication skills to translate operational logistics workflows into AI capabilities.