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About PureFacts Financial Solutions
PureFacts is the leader in the Revenue Performance Management category for wealth and asset management firms. The PureRevenue™ Platform helps organizations maximize revenue potential by connecting pricing, billing, compensation, advisor behavior, and AI-powered intelligence within a single Revenue Book of Record. By transforming fragmented revenue processes into a coordinated growth system, firms gain greater visibility, stronger pricing discipline, improved revenue capture, and more effective advisor alignment. The result is faster organic growth, improved profitability, and increased enterprise value. For more than 25 years, PureFacts has helped leading financial institutions turn revenue from an operational process into a strategic advantage.
At PureFacts, we are building an AI-native platform and company. We embed AI, intelligent automation, and agentic workflows across our products and operations to detect anomalies, surface insights, streamline repetitive work, and support faster, better decision-making. In a highly regulated industry, we believe AI must be practical, governed, and auditable—amplifying human expertise while helping our teams and clients focus on higher-value, strategic work.
About the role
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. Builds evaluation and safety frameworks. 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.
What you'll do
LLM & Agent Development
Design and build LLM-powered applications and AI agents for both internal and client-facing use casesDevelop solutions such as:AI copilots for internal teams and clientsIntelligent workflow automation agentsNatural language interfaces for data and reportingImplement prompt engineering, tool usage, and agent orchestration frameworksAI-First Automation & Use Cases
Identify opportunities to replace manual processes with AI-driven automationBuild systems that enable users to interact with complex data through natural languageDevelop AI solutions that enhance:Revenue insights and analyticsClient reporting and communicationOperational efficiency across workflowsSystem Design & Integration
Integrate LLMs into PureFacts’ SaaS platform and data systemsBuild APIs and services to support AI-powered featuresWork with data and engineering teams to ensure secure, scalable, and reliable integrationsRetrieval-Augmented Generation (RAG) & Data Integration
Design and implement RAG pipelines using structured and unstructured data sourcesWork with:Vector databases (e.g., Pinecone, Weaviate)Embedding models and semantic searchEnsure accurate, relevant, and context-aware outputs from AI systemsEvaluation, Testing & Optimization
Develop frameworks to evaluate LLM outputs for quality, accuracy, and reliabilityContinuously optimize prompts, models, and workflowsMonitor system performance and implement improvementsAI Infrastructure & Tooling
Leverage and integrate tools such as:OpenAI, Azure OpenAI, or similar LLM providersLangChain, LlamaIndex, or agent frameworksAPIs, microservices, and cloud infrastructureCollaborate with MLOps to ensure scalable and maintainable deploymentsResponsible AI & Governance
Ensure AI solutions are secure, compliant, and aligned with responsible AI principlesAddress:Data privacy and securityModel hallucination and reliabilityExplainability and transparencyCross-Functional Collaboration
Partner with Product, Engineering, and Client teams to translate AI capabilities into business valueHelp stakeholders identify opportunities to increase efficiency and reduce manual effortCommunicate technical concepts in a clear, practical wayQualifications
Experience1-3 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 contextsFamiliar with agent frameworks (LangGraph or similar)Hands-on experience building LLM-based applications or AI agentsExperience in SaaS, fintech, or data-driven environments is preferredTechnical Skills
Strong programming skills in Python (required)Experience with:LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.)Prompt engineering and agent frameworks (LangChain, LlamaIndex, etc.)APIs and microservices architectureData processing (SQL, Python data libraries)Familiarity with:Vector databases and embeddingsCloud platforms (AWS, Azure, GCP)AI & Agent Expertise
Experience building:Retrieval-Augmented Generation (RAG) systemsMulti-step agent workflowsTool-using agents and automation systemsStrong understanding of:LLM limitations and optimization techniquesEvaluation methods for generative AIAutomation & Product Mindset
Passion for using AI to automate workflows and eliminate low-value workAbility to translate AI capabilities into practical, high-impact solutionsStrong focus on user experience and real-world applicationCommunication & Collaboration
Ability to work across technical and non-technical teamsStrong problem-solving and systems thinking skillsClear communication of complex AI conceptsEducation
Degree in Computer Science, Engineering, Data Science, or related fieldAdvanced degree is a plus but not requiredKey Success Metrics
Deployment of AI-powered copilots and agents into productionReduction in manual effort through AI-driven automationAdoption and usage of AI features by internal teams and clientsQuality, reliability, and accuracy of AI-generated outputsSpeed of development and iteration of AI solutionsThe pay range for this role is:80,000 - 100,000 CAD per year(Toronto, Canada)PI0e1ba475cf6d-30511-41333475
