Purefacts Financial Solutions→
AI Engineer at Purefacts Financial Solutions in Toronto, Ontario, CA
Skills
Job Description
About PureFacts Financial Solutions
PureFacts is a revenue performance software company serving wealth management, asset management, and asset servicing firms. We help financial institutions protect, optimize, and grow revenue through a connected platform spanning pricing, billing, compensation, reporting, and transparency. By unifying fragmented data and workflows into a trusted revenue foundation, we help clients improve accuracy, strengthen governance, reduce manual effort, and unlock new growth opportunities.
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 cases
- Develop solutions such as:
- AI copilots for internal teams and clients
- Intelligent workflow automation agents
- Natural language interfaces for data and reporting
- AI copilots for internal teams and clients
- Implement prompt engineering, tool usage, and agent orchestration frameworks
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:
- Revenue insights and analytics
- Client reporting and communication
- Operational efficiency across workflows
- Revenue insights and analytics
System Design & Integration
- 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
- Work with:
- Vector databases (e.g., Pinecone, Weaviate)
- Embedding models and semantic search
- Vector databases (e.g., Pinecone, Weaviate)
- Ensure accurate, relevant, and context-aware outputs from AI systems
Evaluation, Testing & Optimization
- Develop frameworks to evaluate LLM outputs for quality, accuracy, and reliability
- Continuously optimize prompts, models, and workflows
- Monitor system performance and implement improvements
AI Infrastructure & Tooling
- Leverage and integrate tools such as:
- OpenAI, Azure OpenAI, or similar LLM providers
- LangChain, LlamaIndex, or agent frameworks
- APIs, microservices, and cloud infrastructure
- OpenAI, Azure OpenAI, or similar LLM providers
- Collaborate with MLOps to ensure scalable and maintainable deployments
Responsible AI & Governance
- Ensure AI solutions are secure, compliant, and aligned with responsible AI principles
- Address:
- Data privacy and security
- Model hallucination and reliability
- Explainability and transparency
- Data privacy and security
Cross-Functional Collaboration
- 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 clear, practical way
Qualifications
- Experience
- 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
- Familiar with agent frameworks (LangGraph or similar)
- Hands-on experience building LLM-based applications or AI agents
- Experience in SaaS, fintech, or data-driven environments is preferred
Technical 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 architecture
- Data processing (SQL, Python data libraries)
- LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.)
- Familiarity with:
- Vector databases and embeddings
- Cloud platforms (AWS, Azure, GCP)
- Vector databases and embeddings
AI & Agent Expertise
- Experience building:
- Retrieval-Augmented Generation (RAG) systems
- Multi-step agent workflows
- Tool-using agents and automation systems
- Retrieval-Augmented Generation (RAG) systems
- Strong understanding of:
- LLM limitations and optimization techniques
- Evaluation methods for generative AI
- LLM limitations and optimization techniques
Automation & Product Mindset
- 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
Communication & Collaboration
- Ability to work across technical and non-technical teams
- Strong problem-solving and systems thinking skills
- Clear communication of complex AI concepts
Education
- Degree in Computer Science, Engineering, Data Science, or related field
- Advanced degree is a plus but not required
Key Success Metrics
- Deployment of AI-powered copilots and agents into production
- Reduction in manual effort through AI-driven automation
- Adoption and usage of AI features by internal teams and clients
- Quality, reliability, and accuracy of AI-generated outputs
- Speed of development and iteration of AI solutions