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- 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.
Benefits- Competitive Salary
- Healthcare Benefit Package
- Career Growth
