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Entrylevel Retrieval Augmented Generation Jobs in Toronto, ON

Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache ...

Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache ...

Implement prompt engineering strategies, retrieval-augmented generation (RAG) patterns, and modern AI architectures. * Generate business insights through data exploration, experimentation, and ...

Knowledge or hands-on experience with Deep Learning architectures and Generative AI (e.g., LLMs, building Retrieval-Augmented Generation (RAG) pipelines). Retail Domain Expertise: Previous experience ...

... Retrieval-Augmented Generation (RAG), and agentic AI systems, combined with strong software-engineering fundamentals and demonstrated ownership of production applications. This role will focus on ...

... Retrieval-Augmented Generation (RAG), and agentic AI systems, combined with strong software-engineering fundamentals and demonstrated ownership of production applications. This role will focus on ...

Work hands-on with agent frameworks, retrieval-augmented generation pipelines, and LLM-powered systems in production. - Entrepreneurial team:We move fast, experiment often, and ship real products.

Cloud/AI Platform Engineer

Toronto, ON · On-site

CA$75K - CA$141K/yr

New Graduate / Entry Level About the Role Are you looking to make your mark in AI? Are you an AI ... Retrieval-Augmented Generation (RAG) concepts Salary : $75,900.00 - $141,900.00 Pay Type: Salaried ...

Cloud/AI Platform Engineer

Toronto, ON · On-site

CA$70K - CA$150K/yr

New Graduate / Entry Level About the Role Are you looking to make your mark in AI? Are you an AI ... Retrieval-Augmented Generation (RAG) concepts Salary : $70,000.00 - $150,000.00 Pay Type: Salaried ...

New

Basic knowledge of Agentic AI concepts such as prompting, Retrieval-Augmented Generation, tool calling and structured outputs. * Experience with Git, GitHub Actions, CI/CD pipelines, Docker or Podman ...

Working knowledge of retrieval-augmented generation: embeddings, chunking, retrieval quality, and the reasons a retrieval system returns the wrong thing. * Strong backend engineering fundamentals ...

AI Design Engineer - AVP

Mississauga, ON · On-site

CA$94K - CA$141K/yr

RAG: Understanding of Retrieval-Augmented Generation (RAG) principles, including vector databases and advanced retrieval techniques. CI/CD: Experience with containerization and deployment ...

Summer Intern 2027 - AI

Toronto, ON · Hybrid

CA$54K - CA$72K/yr

Familiarity with generative AI concepts (e.g., prompt engineering, retrieval-augmented generation) * Experience with cloud platforms (e.g., AWS, Azure, or Google Cloud) or data tools (e.g ...

Winter Co-op 2027 - AI

Toronto, ON · Hybrid

CA$54K - CA$72K/yr

Familiarity with generative AI concepts (e.g., prompt engineering, retrieval-augmented generation) * Experience with cloud platforms (e.g., AWS, Azure, or Google Cloud) or data tools (e.g ...

Familiarity with generative AI concepts (e.g., prompt engineering, retrieval-augmented generation) * Experience with cloud platforms (e.g., AWS, Azure, or Google Cloud) or data tools (e.g ...

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Senior AI Engineer

Charger Logistics Inc

Brampton, ON • On-site

Full-time

Posted 7 days ago


Job 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 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