The AI Engineer (LLM/Agent) will own the conversational layer that describes Purefacts' ML model outputs to end users, develop a "Revenue Assistant" Agent from R&D; through to prototype, and design context architecture grounded in client-specific pricing data. This role sits at the intersection of machine learning, software engineering, and product, focusing on building intelligent systems that can reason, automate workflows, and augment human decision‑making. You will play a key role in advancing PureFacts' AI-first strategy, developing AI-powered copilots, agents, and automation tools that reduce manual work, improve productivity, and deliver meaningful client value. Design and build LLM-powered applications and AI agents for both internal and client-facing use cases AI copilots for internal teams and clients Natural language interfaces for data and reporting ~ AI-First Automation &

• Use Cases Identify opportunities to replace manual processes with AI-driven automation Build systems that enable users to interact with complex data through natural language Develop AI solutions that enhance: Integrate LLMs into PureFacts’ SaaS platform and data systems Build APIs and services to support AI-powered features Work with data and engineering teams to ensure secure, scalable, and reliable integrations Retrieval-Augmented Generation (RAG) &

• Data Integration Design and implement RAG pipelines using structured and unstructured data sources Vector databases (e.g., Ensure accurate, relevant, and context-aware outputs from AI systems Evaluation,



Testing &

• Optimization Develop frameworks to evaluate LLM outputs for quality, accuracy, and reliability Monitor system performance and implement improvements AI Infrastructure &

• Tooling Collaborate with MLOps to ensure scalable and maintainable deployments Responsible AI &

• Governance Ensure AI solutions are secure, compliant, and aligned with responsible AI principles Data privacy and security Partner with Product, Engineering, and Client teams to translate AI capabilities into business value Help stakeholders identify opportunities to increase efficiency and reduce manual effort Communicate technical concepts in a transparent, practical way 1-3 years of LLM application development - RAG pipelines, vector databases, agent orchestration (tool-use, multi-step reasoning) ~ Experience with evaluation frameworks for generative AI, and in putting guardrails/safety in regulated contexts ~ Hands-on experience building LLM-based applications or AI agents ~ Experience in SaaS, fintech, or data-driven environments is preferred AI &

• Agent Expertise Experience building: LLM limitations and optimization techniques Evaluation methods for generative AI Passion for using AI to automate workflows and eliminate low-value work Ability to translate AI capabilities into practical, high-impact solutions Strong focus on user experience and real-world application Ability to work across technical and non-technical teams Clear communication of complex AI concepts

📌 Copy of AI Engineer (Toronto)
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📍 Toronto