ZipRecruiter

Log In

1

Retrieval Augmented Generation Jobs in Ontario (NOW HIRING)

Maarut

Experience developing Retrieval Augmented Generation (RAG) solutions using vector databases and semantic search. \n * Experience developing AI agents using Model Context Protocol (MCP). \n

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 ...

Guidepoint

AI Engineer

Toronto, ON

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

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 · On-site

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 ...

The Home Depot Canada

CA$130K - CA$160K/yr

Design and implement machine learning models, NLP, LLM-powered applications, retrieval-augmented generation (RAG), prompt engineering, and other AI techniques to improve user experience and insights ...

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 ...

Alquemy

Full Stack Engineer

Toronto, ON

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 ...

next page

Showing results 1-20

All JobsRetrieval Augmented Generation JobsRetrieval Augmented Generation Jobs in Ontario

Retrieval Augmented Generation information

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 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 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 popular job titles related to Retrieval Augmented Generation jobs in Ontario? For Retrieval Augmented Generation jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Retrieval Augmented Generation jobs in Ontario look for? The top searched job categories for Retrieval Augmented Generation jobs in Ontario are:
What cities in Ontario are hiring for Retrieval Augmented Generation jobs? Cities in Ontario with the most Retrieval Augmented Generation job openings:
Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Ontario as of August 2026, with employment types broken down into 72% Full Time, 26% Part Time, and 2% Contract. Highlights an 62% Physical, 3% Hybrid, and 35% Remote job distribution.

RQ00735 - Software Developer - Artificial Intelligence (AI) - Senior

Maarut

On-site

Contractor

Posted 7 days ago

Job description

\n <\/head>\n \n

Must Haves:<\/span>
<\/p>\n

    \n
  • Senior software development experience designing and implementing enterprise AI solutions.<\/span>
    <\/li>\n
  • Experience developing Retrieval Augmented Generation (RAG) solutions using vector databases and semantic search.<\/span>
    <\/li>\n
  • Experience developing AI agents using Model Context Protocol (MCP).<\/span>
    <\/li>\n
  • Experience integrating Large Language Models with enterprise applications.<\/span>
    <\/li>\n
  • Experience implementing healthcare AI solutions using FHIR R4\/R4B, SMART on FHIR, and REST APIs.<\/span>
    <\/li>\n
  • Experience developing Java Spring Boot and Python backend services supporting AI workloads.<\/span>
    <\/li>\n
  • Experience with Health Services Directories<\/span>
    <\/li>\n
  • Experience with AWS AI services including Amazon Bedrock and related AI services.<\/span>
    <\/li>\n
  • Experience implementing vector search using OpenSearch <\/span>
    <\/li>\n
  • Experience designing prompt orchestration, tool calling, and agent workflows.<\/span>
    <\/li>\n
  • Experience implementing secure AI applications using OAuth2\/OIDC and role\-based authorization.<\/span>
    <\/li>\n
  • Experience integrating AI solutions with healthcare provider directories and structured healthcare data.<\/span>
    <\/li>\n
  • Experience evaluating AI accuracy, hallucination reduction, grounding, and response quality.<\/span>
    <\/li>\n
  • Experience with AWS Bedrock<\/span>
    <\/li>\n <\/ul>

    Responsibilities<\/span>:
    <\/p>\n

      \n
    • Design and develop AI agents supporting intelligent provider and referral destination discovery.<\/span>
      <\/li>\n
    • Develop Retrieval Augmented Generation (RAG) pipelines utilizing PHSD healthcare data.<\/span>
      <\/li>\n
    • Design and implement MCP servers exposing PHSD functionality to AI clients.<\/span>
      <\/li>\n
    • Develop semantic search capabilities supporting provider, practitioner, organization, healthcare service, and specialty discovery.<\/span>
      <\/li>\n
    • Integrate AI agents with Smile CDR, AWS HealthLake, PHSD APIs, Provider Registry, and Master Data Management services.<\/span>
      <\/li>\n
    • Develop intelligent referral destination recommendations based on referral reason, specialty, geography, language, service availability, and organizational relationships.<\/span>
      <\/li>\n
    • Develop AI services that validate referral completeness and identify missing referral information prior to submission.<\/span>
      <\/li>\n
    • Implement explainable AI responses referencing authoritative PHSD records and supporting evidence.<\/span>
      <\/li>\n
    • Develop prompt templates, orchestration workflows, tool selection strategies, and AI evaluation frameworks.<\/span>
      <\/li>\n
    • Optimize retrieval quality, ranking, semantic search relevance, latency, and operational performance.<\/span>
      <\/li>\n
    • Implement monitoring, evaluation, telemetry, audit logging, and continuous improvement processes for AI services.<\/span>
      <\/li>\n
    • Produce technical documentation, AI governance documentation, operational procedures, and knowledge transfer materials.<\/span>
      <\/li>\n <\/ul>

      Desired Skills:<\/span>
      <\/p>\n

        \n
      • Experience developing AI solutions for healthcare provider directory, referral, scheduling, or care navigation systems. <\/span>
        <\/li>\n
      • Experience implementing healthcare knowledge graphs or semantic healthcare search. <\/span>
        <\/li>\n
      • Experience with Amazon Bedrock Agents or other enterprise AI orchestration platforms. <\/span>
        <\/li>\n
      • Experience implementing MCP\-based enterprise integrations. <\/span>
        <\/li>\n
      • Experience developing AI evaluation frameworks including groundedness, factuality, retrieval accuracy, and hallucination detection. <\/span>
        <\/li>\n
      • Experience implementing FHIR terminology services, SNOMED CT, LOINC, and healthcare ontologies. <\/span>
        <\/li>\n
      • Experience with clinician\-facing AI assistants and decision support tools. <\/span>
        <\/li>\n
      • Experience with Responsible AI, privacy\-preserving AI, explainability, and AI governance. <\/span>
        <\/li>\n
      • Experience working with Agile delivery teams building enterprise healthcare AI platforms.<\/span>
        <\/li>\n <\/ul>

        Required Skills:<\/span>
        <\/p>\n

          \n
        • Enterprise AI application development using modern LLM frameworks and agent architectures. <\/span>
          <\/li>\n
        • Retrieval Augmented Generation (RAG) architecture, semantic retrieval, vector embeddings, and knowledge grounding. <\/span>
          <\/li>\n
        • Model Context Protocol (MCP) server development, tool integration, and agent orchestration. <\/span>
          <\/li>\n
        • Prompt engineering, tool calling, structured outputs, evaluation frameworks, and AI workflow design. <\/span>
          <\/li>\n
        • Integration of AI solutions with FHIR R4\/R4B, SMART on FHIR, REST APIs, and healthcare interoperability standards. <\/span>
          <\/li>\n
        • Java Spring Boot and\/or Python backend development supporting AI services. <\/span>
          <\/li>\n
        • Amazon Bedrock, OpenSearch vector search, embeddings, inference APIs, and enterprise AI deployment. <\/span>
          <\/li>\n
        • Healthcare provider directory, referral workflows, practitioner, organization, healthcare service, and location data modelling. <\/span>
          <\/li>\n
        • Secure API development using OAuth2, OpenID Connect, JWT, and enterprise authentication patterns. <\/span>
          <\/li>\n
        • Cloud\-native AWS development, monitoring, logging, CI\/CD, and operational support. <\/span>
          <\/li>\n
        • Strong analytical, problem solving, communication, and technical documentation skills.<\/span>
          <\/li>\n <\/ul>

          Evaluation Criteria:<\/span>
          <\/p>\n

            \n
          • Enterprise AI development experience, including Retrieval Augmented Generation (RAG), Large Language Models (LLMs), semantic search, prompt engineering, AI agent development, Model Context Protocol (MCP), tool orchestration, and AI evaluation frameworks. <\/span><\/span>25 Points
            <\/li>\n
          • Healthcare interoperability experience, including FHIR R4\/R4B, SMART on FHIR, Smile CDR, AWS HealthLake, REST APIs, Provider Registry, Master Data Management, healthcare terminology, and healthcare data integration. <\/span><\/span>25 <\/span>Points
            <\/li>\n
          • Cloud application development experience, including Java\/Spring Boot and\/or Python, AWS cloud services, Amazon Bedrock, OpenSearch vector search, secure API development, CI\/CD, monitoring, and scalable cloud\-native architectures. <\/span><\/span>25 <\/span>Points
            <\/li>\n
          • Experience working with digital health assets Hospital Report Manager (HRM), Enterprise Master Data Management, Consent Management Service, provincial identity services, and integration with Epic, Oracle Health (Cerner), MEDITECH, or other healthcare information systems. <\/span><\/span>25 <\/span>Points
            <\/li>\n <\/ul>\n
            \n
            \n <\/div><\/span>
            Requirements<\/h3>

            Deliverables:<\/span>
            <\/p>\n

              \n
            • AI architecture, technical design documentation, and implementation plans supporting PHSD Referral Intelligence. <\/span>
              <\/span><\/li>\n
            • AI agents capable of assisting users in identifying appropriate referral destinations using PHSD provider directory information. <\/span>
              <\/span><\/li>\n
            • Retrieval Augmented Generation (RAG) pipelines utilizing PHSD, Smile CDR, AWS HealthLake, and other approved healthcare data sources. <\/span>
              <\/span><\/li>\n
            • Model Context Protocol (MCP) servers exposing PHSD capabilities for AI\-assisted applications. <\/span>
              <\/span><\/li>\n
            • Semantic search implementation supporting provider, practitioner, organization, healthcare service, specialty, and location discovery. <\/span>
              <\/span><\/li>\n
            • Vector embedding generation, indexing, retrieval optimization, and relevance tuning. <\/span>
              <\/span><\/li>\n
            • AI services capable of validating referral destinations, identifying incomplete referral information, and recommending appropriate providers or healthcare services. <\/span>
              <\/span><\/li>\n
            • Integration with PHSD FHIR APIs, Smile CDR, AWS HealthLake, Provider Registry, Master Data Management services, and other provincial digital health assets. <\/span>
              <\/span><\/li>\n
            • Prompt libraries, orchestration workflows, tool definitions, and structured AI interaction patterns. <\/span>
              <\/span><\/li>\n
            • AI evaluation framework including groundedness, retrieval quality, factual accuracy, hallucination detection, citation validation, and response quality metrics. <\/span>
              <\/span><\/li>\n
            • Secure authentication and authorization integration supporting SMART on FHIR, OAuth2\/OIDC, and role\-based access controls. <\/span>
              <\/span><\/li>\n
            • Operational dashboards, monitoring, telemetry, audit logging, and performance reporting for AI services. <\/span>
              <\/span><\/li>\n
            • Unit testing, integration testing, AI evaluation testing, deployment automation, and CI\/CD pipeline support. <\/span>
              <\/span><\/li>\n
            • Technical documentation, operational procedures, AI governance documentation, knowledge transfer materials, and production implementation support.<\/span>
              <\/span><\/li>\n <\/ul>\n
              \n
              \n <\/div>\n
              \n
              \n <\/div>

              Must Haves:
              <\/p>\n

                \n
              • Senior software development experience designing and implementing enterprise AI solutions.<\/span>
                <\/span><\/li>\n
              • Experience developing Retrieval Augmented Generation (RAG) solutions using vector databases and semantic search.<\/span>
                <\/span><\/li>\n
              • Experience developing AI agents using Model Context Protocol (MCP).<\/span>
                <\/span><\/li>\n
              • Experience integrating Large Language Models with enterprise applications.<\/span>
                <\/span><\/li>\n
              • Experience implementing healthcare AI solutions using FHIR R4\/R4B, SMART on FHIR, and REST APIs.<\/span>
                <\/span><\/li>\n
              • Experience with Health Services Directories<\/span>
                <\/span><\/li>\n
              • Experience with AWS AI services including Amazon Bedrock and related AI services.<\/span>
                <\/span><\/li>\n
              • Experience designing prompt orchestration, tool calling, and agent workflows.<\/span>
                <\/span><\/li>\n
              • Experience integrating AI solutions with healthcare provider directories and structured healthcare data.<\/span>
                <\/span><\/li>\n
              • Experience evaluating AI accuracy, hallucination reduction, grounding, and response quality.<\/span>
                <\/span><\/li>\n
              • Experience with AWS Bedrock<\/span>
                <\/span><\/li>\n <\/ul><\/span>
                \n <\/body>\n<\/html>

Apply