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Modoras Accounting Syd Logo Modoras Accounting Syd · IT - Software Development

AI Engineer

Canada, Toronto · Job Posted February 18, 2026
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Job Description

Guidepoint seeks an experienced AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products. This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products.

Job Responsibility

  • Architect and Build Production Systems: 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 AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production environments like Databricks and Azure Kubernetes Service (AKS)
  • Optimize RAG Pipelines: Continuously improve retrieval and generation quality through techniques like retrieval optimization (tuning k-values, chunk sizes), using re-rankers, advanced chunking strategies, and prompt engineering for hallucination reduction
  • Integrate Intelligent Systems: Engineer solutions that seamlessly combine LLMs with our proprietary knowledge repositories, external APIs, and real-time data streams to create powerful copilots and research assistants
  • Champion LLMOps and Engineering Best Practices: Collaborate with data science and engineering teams to establish and implement best practices for LLMOps, including automated evaluation using frameworks like LLM Judges or MLflow, AI observability, and system monitoring
  • Evaluate and Implement AI Strategies: Systematically evaluate and apply advanced prompt engineering methods (e.g., Chain-of-Thought, ReAct) and other model interaction techniques to optimize the performance and safety of proprietary and open-source LLMs
  • Mentor and Lead: Provide technical leadership to junior engineers through rigorous code reviews, mentorship, and design discussions, helping to elevate the team's engineering standards
  • Influence the Roadmap: Partner closely with product and business stakeholders to translate user needs into technical requirements, define priorities, and shape the future of our AI product offerings

Requirements

  • Bachelor’s degree in Computer Science, Engineering, or a related technical field with 6+ years of professional experience
  • or a Master’s degree with 4+ years of professional experience in backend software engineering and Generative AI
  • Proven track record of designing, building, and scaling distributed, production-grade systems
  • Deep expertise in Python, a major backend framework (e.g., FastAPI, Flask), and asynchronous programming (e.g., asyncio)
  • Proficiency in designing RESTful APIs, microservices, and the complete operational lifecycle, including comprehensive testing, CI/CD (e.g., ArgoCD), observability, monitoring, alerting, maintaining high uptime, and executing zero-downtime deployments
  • Hands-on experience deploying and managing applications on a major cloud platform (Azure preferred, AWS/GCP acceptable) using containerization (Docker) and orchestration (Kubernetes, Helm)
  • 2+ years of experience building applications that leverage large language models from providers like OpenAI, Anthropic, or Google Gemini
  • Direct experience with modern LLM patterns such as retrieval-augmented generation (RAG), hybrid search using vector databases (e.g., Pinecone, Elasticsearch), multi-agent AI systems with tool calls, and prompt engineering
  • Experience designing and implementing robust evaluation frameworks for LLM-based systems, including rubric-based scoring, LLM Judges, or using tools like MLflow, alongside monitoring for performance and drift
  • Familiarity with large-scale data processing platforms and tools (e.g., Databricks, Apache Spark)
  • Practical experience with libraries and frameworks like LangChain or LlamaIndex for building LLM-powered applications
  • Demonstrated ability to lead complex technical projects and foster the growth of other engineers

What we offer

  • Paid Time Off
  • Comprehensive benefits plan
  • Company RRSP Match
  • Development opportunities through the LinkedIn Learning platform
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