Charger Logistics Inc.
Transportation/Trucking/Railroad
AIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Engineer at Charger Logistics Inc.. Skills: AI agents, MCP integrations, Logistics automation. Design MCP servers. Expose domain services”
What You'll Achieve.
Improve reliability; Improve transparency; Improve efficiency
Industry & Context.
What They're Looking For.
Must Have
Bachelor's in Computer Science, 5+ years Python, LLM integration proficiency, RAG knowledge, SQL proficiency, Cloud platforms experience, Kubernetes experience
Nice to Have
MCP background, Agent orchestration frameworks background, Knowledge graphs background, Streaming data systems background
What You'll Do.
Expose domain services
Build multi-agent workflows
Develop knowledge retrieval pipelines
Design hybrid retrieval architectures
Implement LLM integration layers
Collaborate with cross-functional teams
Deploy agent infrastructure
Maintain agent infrastructure
How You'll Work.
Team & Collaboration
Cross-functional teams
Full Job Description
Charger logistics Inc. is a world- class asset-based carrier with locations across North America. With over 20 years of experience providing the best logistics solutions, Charger logistics has transformed into a world-class transport provider and continue to grow. We are looking for a highly motivated AI Engineer to join our team based out of our **Brampton office** and contribute to the development of AI-driven solutions for various departments. This role focuses on building production AI agents and MCP (Model Context Protocol) integrations that automate real logistics workflows—dispatch, billing, compliance, and fleet operations—improving the reliability, transparency, and efficiency of AI applications in real-world, high-stakes environments. **Responsibilities:** * Design, develop, and deploy MCP servers exposing domain services as AI-consumable tools with proper authentication, observability, and error handling. * Build multi-agent workflows using orchestration frameworks and agent-to-agent communication protocols for complex logistics automation. * Develop and optimize knowledge retrieval pipelines using RAG, KAG, and CAG strategies—selecting the right approach based on query complexity, data volatility, and domain reasoning requirements. * Design hybrid retrieval architectures that route between CAG for static reference data, RAG for dynamic operational queries, and KAG for multi-hop reasoning across structured domain knowledge. * Implement LLM integration layers—prompt engineering, function calling, structured output parsing, and model routing for domain accuracy. * Collaborate with cross-functional teams to collect requirements and translate operational workflows into agent capabilities. * Deploy and maintain agent infrastructure on Kubernetes with GitOps practices and observability tooling. **Requirements** * Bachelor's in Computer Science, Artificial Intelligence, or a related technical field. * Strong communication skills and experience working in interdi
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