AI Engineer

Charger Logistics Inc

Job Overview

Location

Brampton, Ontario, Canada

Employment Type

Full-time

Work Arrangement

On-site

Sector

Information Technology & Software

Experience Level

Mid-level (3-5 years)

Application Deadline

June 27, 2026

About the Company

Charger Logistics Inc. is a distinguished, asset-based carrier with a significant presence across North America. With over two decades of dedicated experience, the company has established itself as a premier provider of logistics solutions, evolving into a world-class transport provider that continues to expand its operations. Their commitment to excellence drives them to innovate and integrate advanced technologies to enhance their services. The company fosters an environment where continuous growth and development are prioritized, offering opportunities for career advancement and professional development within the dynamic logistics industry.

Job Description

Charger Logistics Inc. is a world-class, asset-based carrier with extensive operations across North America. Leveraging over 20 years of experience, the company has become a leading logistics solutions provider and continues its trajectory of growth.

We are seeking a highly motivated AI Engineer to join our Brampton-based team. This role is instrumental in developing AI-driven solutions that automate critical logistics workflows, including dispatch, billing, compliance, and fleet operations. You will focus on building production AI agents and MCP (Model Context Protocol) integrations to enhance the reliability, transparency, and efficiency of AI applications in real-world scenarios.

To apply for this role, click the Apply button on this page and follow the instructions.

Required Skills

PythonAI/MLREST APIsMicroservicesLLM IntegrationRAGKAGCAGSQLKubernetesMCPAgent OrchestrationKnowledge Graphs

Key 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.

Qualifications

Bachelor's in Computer Science, Artificial Intelligence, or a related technical field.
Strong communication skills and experience working in interdisciplinary or team-based environments.
Solid understanding of REST APIs, microservices architecture, and AI/ML concepts.
Experience building production-grade AI applications in Python—not just notebooks or prototypes.
Hands-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 & Perks

Competitive Salary
Healthcare Benefit Package
Career Growth

How to Apply

To apply for this role, click the Apply button on this page and follow the instructions.

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The Canadian logistics sector is experiencing rapid digital transformation, driven by the need for enhanced efficiency and predictive capabilities. This AI Engineer role is pivotal in developing cutting-edge AI-driven solutions that directly impact operational ROI. You will focus on building production AI agents and MCP integrations to automate critical logistics workflows, including dispatch, billing, compliance, and fleet operations. Your work will significantly improve the reliability, transparency, and overall efficiency of AI applications in a high-stakes, real-world environment, contributing to significant business growth and leadership scale.