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Senior GenAI Engineer

  • Ontario, Toronto

  • 03/05/2026

  • Contract

  • Active

Job Description:

  • JOB SUMMARY
    The Senior GenAI Engineer will lead the design and development of large language model (LLM) based solutions to support an AI-powered recommendation and assistant platform for insurance sales advisors. This role focuses on architecting scalable GenAI pipelines from scratch, including Retrieval-Augmented Generation (RAG), agentic workflows, and LLM integrations. The engineer will work closely with data, infrastructure, and engineering teams to develop production-ready AI solutions, evaluate models, and implement robust GenAI microservices within a secure enterprise cloud environment. The position requires strong expertise in machine learning, NLP, GenAI technologies, and cloud-native architectures, with the ability to translate business needs into scalable AI solutions.

    KEY RESPONSIBILITIES
    • Architect and design LLM-based solutions including RAG pipelines, embeddings, fine-tuning strategies, and evaluation frameworks  
    • Build scalable GenAI microservices and integrate them with internal enterprise systems  
    • Develop advanced prompt engineering strategies, agentic workflows, and safety guardrails for LLM systems  
    • Evaluate open-source and commercial LLM models for performance, cost efficiency, and risk mitigation  
    • Collaborate with data teams to prepare training datasets, knowledge bases, and analytics pipelines  
    • Oversee ingestion and refresh of knowledge bases and data pipelines supporting RAG architectures  
    • Implement monitoring, validation frameworks, and closed-loop measurement to continuously improve AI solution quality  
    • Ensure compliance with enterprise security standards and insurance regulatory requirements  
    • Work with engineering teams to productionize GenAI solutions using scalable cloud architectures  
    • Partner with business stakeholders to define use cases, requirements, and solution delivery strategies  
    • Document architecture, data sources, and technical processes supporting AI solutions  
    • Present insights, model performance metrics, and business impact to senior stakeholders  
    • Support change management and adoption strategies for AI-driven solutions  
    • Mentor junior engineers and promote best practices in AI engineering and system design  
    • Collaborate with cross-functional teams including Data Infrastructure, Backend, and Frontend teams  
    • Maintain accurate task tracking and documentation using tools such as Jira  
    REQUIRED QUALIFICATIONS
    • 6+ years of experience in machine learning, artificial intelligence, or NLP engineering  
    • Minimum 2+ years of hands-on experience working with GenAI and LLM-based applications  
    • Strong expertise in Retrieval-Augmented Generation (RAG), vector databases, embeddings, and model evaluation techniques  
    • Hands-on experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, Llama, or similar models  
    • Experience designing and deploying GenAI solutions in production environments  
    • Proficiency in Python and experience building data pipelines for AI systems  
    • Strong knowledge of cloud-native architectures, preferably on Microsoft Azure  
    • Experience building scalable AI systems in enterprise environments  
    • Strong understanding of machine learning techniques and statistical modeling methods  
    • Experience working with SQL, data modeling, and relational or NoSQL databases  
    • Strong problem-solving, analytical thinking, and communication skills  
    • Ability to collaborate effectively with cross-functional teams and business stakeholders  
    • Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience  
    PREFERRED QUALIFICATIONS
    • Experience designing agentic AI systems and integrating AI agents into enterprise workflows  
    • Familiarity with BI and visualization tools such as Power BI or Tableau  
    • Experience working within financial services or insurance domain environments  
    • Experience with Hadoop or other distributed data platforms  
    • Strong understanding of MLOps or GenAIOps practices for managing AI lifecycle  
    CERTIFICATIONS
    Microsoft Azure certification (preferred)

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