Senior 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 GitOps practices and observability tooling.

• 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 (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.

• Competitive Salary
• Healthcare Benefit Package
• Career Growth