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Entry Level Retrieval Augmented Generation Jobs in Toronto, ON

Charger Logistics Inc

AI Engineer

Brampton, ON · On-site

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

Charger Logistics Inc

AI Engineer

Brampton, 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 ...

CorGTA

Data Scientist - 12 month contract

Toronto, ON · On-site

CA$70/hr

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 ...

Amazon

Software Dev Engineer, Sales AI

Toronto, 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.

New

Amazon

Software Dev Engineer, Sales AI

Toronto, 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.

New

Walmart

Senior, Data Scientist

Mississauga, ON · On-site

CA$91K - CA$126K/yr

Experience developing or applying Large Language Model (LLM) and Generative AI solutions, such as retrieval-augmented generation, prompt engineering, embedding-based search, model evaluation, fine ...

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Entry Level Retrieval Augmented Generation information

What is an entry level retrieval augmented generation job?

Entry level retrieval augmented generation jobs involve assisting in the development and optimization of AI systems that combine information retrieval techniques with generative models. Employees in these roles typically help build, test, and maintain systems where AI retrieves relevant data from large databases to enhance the accuracy and relevance of generated responses. These positions often require basic skills in programming, machine learning, and familiarity with natural language processing. They are ideal for recent graduates or those new to AI, offering opportunities to learn about modern AI architectures and contribute to innovative projects. Entry level workers may work under the guidance of senior engineers or researchers, supporting experimentation and evaluation tasks.

What are the key skills and qualifications needed to thrive as an entry level retrieval augmented generation specialist?

To thrive as an Entry Level Retrieval Augmented Generation Specialist, you need a foundational understanding of natural language processing (NLP), information retrieval, and basic programming skills, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, vector databases (like FAISS or Pinecone), and frameworks for large language models (LLMs) is typically required. Strong problem-solving abilities, attention to detail, and effective communication help you collaborate and troubleshoot solutions in team environments. These skills and qualities are crucial for building reliable RAG systems that deliver accurate and relevant information to users.

What is the difference between Entry Level Retrieval Augmented Generation vs Entry Level Data Scientist?

AspectEntry Level Retrieval Augmented GenerationEntry Level Data Scientist
Required CredentialsBasic programming, understanding of NLP and AI conceptsBachelor's in Data Science, Computer Science, or related field
Work EnvironmentTech companies, AI startups, research labsTech firms, finance, healthcare, consulting
Industry UsageAI development, NLP applications, chatbot creationData analysis, predictive modeling, data-driven decision making

Entry Level Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with generative AI, requiring knowledge of NLP and programming. Entry Level Data Scientist involves analyzing data, building models, and deriving insights, often with a broader data analysis skill set. While both roles require technical skills, Retrieval Augmented Generation is more specialized in AI model development, whereas Data Scientists work across various data projects.

What are some common challenges faced by entry-level professionals working in retrieval augmented generation roles?

Entry-level professionals in Retrieval Augmented Generation (RAG) often encounter challenges such as understanding how to effectively combine information retrieval systems with large language models and adapting to rapidly evolving technologies. Balancing accuracy and efficiency when designing or fine-tuning retrieval pipelines can also be a learning curve. Additionally, you may need to collaborate closely with data engineers, machine learning specialists, and product teams to ensure the RAG system aligns with business requirements. Staying proactive in learning and engaging with peers can help overcome these challenges and accelerate career growth.
What are popular job titles related to Entry Level Retrieval Augmented Generation jobs in Toronto, ON? For Entry Level Retrieval Augmented Generation jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching Entry Level Retrieval Augmented Generation jobs in Toronto, ON look for? The top searched job categories for Entry Level Retrieval Augmented Generation jobs in Toronto, ON are:
Entry Level Retrieval Augmented Generation jobs near you
Infographic showing various Entry Level Retrieval Augmented Generation job openings in Toronto, ON as of August 2026, with employment types broken down into 64% Full Time, 34% Part Time, and 2% Contract. Highlights an 67% Physical, 2% Hybrid, and 31% Remote job distribution.

AI Engineer

Charger Logistics Inc

Brampton, ON • On-site

Full-time

Re-posted 4 hours 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