Charger Logistics Inc.
Logistics
AIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Engineer at Charger Logistics Inc.. Skills: AI Engineer, LLM integration, RAG, Kubernetes. Design MCP servers. Develop MCP servers”
What You'll Achieve.
automate real logistics workflows; improving reliability; improving transparency; improving efficiency
Industry & Context.
What They're Looking For.
Must Have
Bachelor's in Computer Science, Artificial Intelligence, communication skills, experience working in interdisciplinary or team-based environments, Solid understanding of REST APIs, microservices architecture, AI/ML concepts, Experience building production-grade AI applications in Python, Hands-on proficiency with LLM integration, function calling, tool use, structured outputs, Solid understanding of knowledge retrieval patterns, RAG (Retrieval-Augmented Generation), Proficiency with SQL, at least one analytical data platform, Experience with cloud platforms, container orchestration (Kubernetes)
Nice to Have
MCP, agent orchestration frameworks, knowledge graphs, streaming data systems, KAG (Knowledge-Augmented Generation), CAG (Cache-Augmented Generation)
What You'll Do.
Build multi-agent workflows
Develop knowledge retrieval pipelines
Design hybrid retrieval architectures
Implement LLM integration layers
Deploy agent infrastructure
Maintain agent infrastructure
How You'll Work.
Team & Collaboration
Collaborate with cross-functional teams; working in interdisciplinary or team-based environments
Communication Scope
communication skills
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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