AI Engineer

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 frameworks

AI-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 workflows

System 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 integrations

Retrieval-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 systems

Evaluation, Testing & Optimization

Develop frameworks to evaluate LLM outputs for quality, accuracy, and reliabilityContinuously optimize prompts, models, and workflowsMonitor system performance and implement improvements

AI 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 deployments

Responsible AI & Governance

Ensure AI solutions are secure, compliant, and aligned with responsible AI principlesAddress:Data privacy and securityModel hallucination and reliabilityExplainability and transparency

Cross-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 way

Qualifications

2+ years building production, customer-facing LLM/GenAI applications, including vector databases, RAG pipelines , agent orchestration6-8+ years of total back-end software engineering experienceDeep, hands-on experience building with agent frameworks (e.g., Microsoft Agent Framework, Google ADK, LangGraph, etc.), including designing custom orchestration patterns beyond out-of-the-box templatesDemonstrated ownership of evaluation frameworks and pipelines and design of deterministic guardrails/safety controls in regulated or compliance-sensitive contextsExperience in SaaS, fintech, or other data-driven, regulated environments strongly preferred

Technical Skills

Expert-level Python (required)Deep experience with:LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.), including cost/latency tradeoffs at scaleAgent development frameworks (LangChain, LlamaIndex, LangGraph, or similar)API and microservices architecture, including integrating LLM and ML model components into larger systemsData processing (SQL, Python data libraries) and pipeline design for both retrieval-quality data and model training dataSolid working knowledge of:Vector databases and embedding strategies (selection, tuning, hybrid search)Cloud platforms (AWS, Azure), including deployment and scaling of AI/ML workloads

AI, Agent & ML Model Expertise

Proven experience architecting and shipping:Retrieval-Augmented Generation (RAG) systems at production scaleMulti-step agent workflows with error handling and recoveryTool-using agents and end-to-end automation systemsStrong, applied understanding of:LLM limitations, failure modes, and optimization techniques (prompt design, fine-tuning vs. RAG tradeoffs, latency/cost optimization)Evaluation methods for generative AI (offline eval sets, human-in-the-loop review, regression testing for prompt/model changes, model performance monitoring)Safety and guardrail design appropriate to regulated environments (PII handling, hallucination mitigation, model bias/fairness checks, audit trails)

Operations & Reliability

Experience operating AI/ML systems in production, including uptime, latency, and cost monitoring for both LLM and model-serving infrastructureFamiliarity with incident response and root-cause analysis for model or agent failures (degraded outputs, drift, hallucination spikes, pipeline breakages)Ability to define and track model/agent health metrics (accuracy, drift, confidence calibration, usage patterns) and act on them proactively

Automation & Product Mindset

Genuine passion for using AI/ML to automate workflows and eliminate low-value workAbility to independently translate ambiguous AI/ML capabilities into practical, high-impact products with minimal guidanceStrong bias toward user experience, reliability, and real-world adoption over technical novelty

Communication & Collaboration

Ability to work fluidly across technical and non-technical teams, including practice/business stakeholdersStrong systems thinking — able to reason about tradeoffs across the full stack, from retrieval/model quality to UX to compliance to operational costComfortable communicating complex AI/ML concepts and tradeoffs to non-technical leadership, and mentoring less-experienced engineers

Key Success Metrics

Successful deployment of AI-powered copilots, agents, and ML-driven products into production, owned end-to-endMeasurable reduction in manual effort through AI/ML-driven automationAdoption and sustained usage of AI/ML features by internal teams and clientsQuality, reliability, and accuracy of AI-generated and model-generated outputs, validated through rigorous evaluation and ongoing monitoringModel/system uptime, drift management, and operational stability in productionSpeed and quality of development/iteration cycles, and contribution to team technical standards

Education

Degree in Computer Science, Engineering, Data Science, or related fieldAdvanced degree is a plus but not required

The pay range for this role is:100,000 - 120,000 CAD per year(Toronto, Canada)

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