AI Engineer

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 Git Ops practices and observability tooling

Requirements

2-3 years of experience with 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 (Big Query, 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 Salary

Healthcare Benefit Package

Career Growth

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