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Retrieval Augmented Generation Jobs in Toronto, ON

Charger Logistics Inc

Data Scientist

Brampton, ON · On-site

Integrate large language models (OpenAI, Google MCP, Ollama, Hugging Face) for conversational analytics, automated insights, and retrieval-augmented generation (RAG) systems. * Operate MLOps ...

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 · 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 ...

Guidepoint

AI Engineer

Toronto, ON · On-site

Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. * Own the ...

Guidepoint

Senior Data/AI Engineer

Toronto, ON · On-site

Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. * Own the ...

Source Code

RQ11252 - Sr. AI Engineer

Toronto, ON

CA$90.18 - CA$108.22/hr

Develops and supports AI Agents leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Model Context Protocol (MCP) servers, tool-calling frameworks and enterprise knowledge ...

Royal Bank of Canada

AI Engineer

Toronto, ON · Hybrid

Deep knowledge of retrieval-augmented generation (RAG), agentic frameworks, context and memory management, and tool/skills integration patterns. * Strong understanding of large language model ...

Chubb

AVP, Lead AI Engineer

Toronto, ON

Hugging Face Transformers, prompt engineering, post-training/fine-tuning pipelines, retrieval-augmented generation (RAG), and agentic AI frameworks. Experience with inference optimization and high ...

Chubb

AVP, Lead AI Engineer

Toronto, ON · On-site

Hugging Face Transformers, prompt engineering, post-training/fine-tuning pipelines, retrieval-augmented generation (RAG), and agentic AI frameworks. Experience with inference optimization and high ...

Synechron

Sr. GenAI Engineer

Toronto, ON · Hybrid

CA$130K - CA$145K/yr

Lead the design and development of innovative machine learning architectures involving zero-shot/few-shot learning, retrieval-augmented generation (RAG), embeddings, and neural networks.

Alquemy

Full Stack Engineer

Toronto, ON · On-site

Build LLM-based solutions, including AI agents, Retrieval-Augmented Generation (RAG), and multi-step workflows. Develop and integrate RESTful APIs and backend services. Collaborate with cross ...

Equinix

Director, AI Solutions

Toronto, ON · Remote

Build systems including LLM-powered copilots, agentic workflows, retrieval-augmented generation pipelines, and predictive models * Own the full lifecycle from data sourcing and model development to ...

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 ...

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Retrieval Augmented Generation information

What is a retrieval augmented generation?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What does a retrieval augmented generation engineer do?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What skills and qualifications are needed for retrieval augmented generation?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Toronto, ON?

The most popular types of Retrieval Augmented Generation jobs in Toronto, ON are:

What are popular job titles related to Retrieval Augmented Generation jobs in Toronto, ON?

For Retrieval Augmented Generation jobs in Toronto, ON, the most frequently searched job titles are:

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Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Toronto, ON as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution.

Data Scientist

Charger Logistics Inc

Brampton, ON • On-site

Full-time

Posted 10 days ago

Job description

Charger Logistics Inc. is a leading asset-based transportation company with over 20 years of experience delivering innovative logistics solutions. We have evolved into a world-class transport provider and continue to expand across North America.

We invest in our people, fostering an environment where learning, growth, and career advancement are encouraged. As an entrepreneurial organization, we value initiative, creativity, and forward-thinking strategies.
We are looking for a Data Scientist to develop, deploy, and scale machine learning (ML) and AI solutions for fleet analytics, logistics optimization, and operational decision-making. This is a hands-on role focusing on production-grade ML, real-time and streaming analytics, and AI-driven decision systems built on cloud platforms, including Google Cloud, Kafka, and RisingWave.

Responsibilities:

  • Design, develop, and deploy production-grade ML models for fleet optimization, including route optimization, ETA prediction, fuel efficiency, capacity planning, predictive maintenance, and driver behavior analysis.
  • Build anomaly detection, forecasting, and time-series models to monitor vehicle health, trip deviations, fuel theft, and demand fluctuations.
  • Develop batch and real-time ML pipelines with low-latency inference using Kafka, RisingWave, and cloud services.
  • Integrate large language models (OpenAI, Google MCP, Ollama, Hugging Face) for conversational analytics, automated insights, and retrieval-augmented generation (RAG) systems.
  • Operate MLOps workflows on Google Cloud using Vertex AI Pipelines, Feature Store, and Model Registry, supporting model training, deployment, monitoring, and drift detection.
  • Build and optimize end-to-end data pipelines for analytics and ML using BigQuery, Dataflow, Dataproc, Vertex AI, Cloud Functions, Pub/Sub, and Cloud Composer (Airflow).
  • Design scalable analytical data models in BigQuery, AlloyDB PostgreSQL, and Snowflake; optimize SQL-based feature engineering, data partitioning, and clustering.
  • Perform exploratory data analysis (EDA) to uncover trends, anomalies, and business insights.
  • Build dashboards and visualizations for stakeholders.
  • Collaborate with cross-functional teams to translate business problems into robust data science solutions.
  • Support best practices in model development, experimentation, documentation, and data governance.

Requirements

  • Bachelor's degree or equivalent in Data Analytics, Statistics, Mathematics, or Computer Science.
  • 4+ years of hands-on experience in data science and machine learning/AI, delivering production-grade ML solutions.
  • Strong experience in Python, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM.
  • Advanced SQL skills, including CTEs, window functions, and query optimization.
  • Hands-on experience with Google Cloud, including Vertex AI (training, pipelines, deployment, feature store) and BigQuery (data modeling, performance tuning).
  • Experience with streaming platforms (Kafka, RisingWave) and Snowflake.
  • Knowledge of anomaly detection, time-series forecasting, optimization, and applied statistical modeling.
  • Experience deploying and monitoring ML models in production, including testing, and working with ETL/orchestration tools like Matillion, Airflow, and Cloud Composer.
  • Familiarity with advanced ML and AI techniques, including LLMs, geospatial or graph ML, computer vision, and GPS data analysis.
  • 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).
  • Experience with Azure, AWS, GCP, Databricks, or multi-cloud deployments is a plus.
  • Excellent communication and problem-solving skills, with the ability to thrive in fast-paced environments.
  • Certifications: Google Cloud Professional Data Engineer or Machine Learning Engineer is an asset; SnowPro Advanced: Data Scientist certification preferred.

Benefits

  • Competitive Salary
  • Healthcare Benefit Package
  • Career Growth

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