Summary
✨ AI‑Generated
A senior AI engineering role focused on turning AI agents into reliable production systems. You will design tool-serving protocol integrations, build multi-agent workflows, and engineer secure, observable, fault-tolerant solutions that automate complex operational processes.
Highlights
Opportunity to build production-grade AI agents and integrations that automate real-world operational workflows, with a focus on reliability, transparency, security, and efficiency.
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 handlingBuild multi-agent workflows using orchestration frameworks and agent-to-agent communication protocols for complex logistics automationDevelop and optimize knowledge retrieval pipelines using RAG, KAG, and CAG strategies—selecting the right approach based on query complexity, data volatility, and domain reasoning requirementsDesign 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 knowledgeImplement LLM integration layers—prompt engineering, function calling, structured output parsing, and model routing for domain accuracyCollaborate with cross-functional teams to collect requirements and translate operational workflows into agent capabilitiesDeploy and maintain agent infrastructure on Kubernetes with GitOps practices and observability tooling
Requirements
2-3 years of experience with Bachelor's in Computer Science, Artificial Intelligence, or a related technical fieldStrong communication skills and experience working in interdisciplinary or team-based environmentsSolid understanding of REST APIs, microservices architecture, and AI/ML conceptsExperience building production-grade AI applications in Python—not just notebooks or prototypesHands-on proficiency with LLM integration{{{{:}}}} function calling, tool use, structured outputs (OpenAI, Anthropic, or Google APIs)Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache-Augmented Generation)Proficiency with SQL and at least one analytical data platform (BigQuery, Snowflake, or similar)Experience with cloud platforms and container orchestration (Kubernetes)Background in MCP, agent orchestration frameworks, knowledge graphs, or streaming data systems is a strong asset
Benefits
Competitive SalaryHealthcare Benefit PackageCareer Growth
