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

- 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

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

- 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

- 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

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)

- Familiarity with:
- - Vector databases and embeddings
- Cloud platforms (AWS, Azure, GCP)

AI & Agent Expertise

- Experience building:
- - Retrieval-Augmented Generation (RAG) systems
- Multi-step agent workflows
- Tool-using agents and automation systems

- Solid understanding of:
- - LLM limitations and optimization techniques
- Evaluation methods for generative AI

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

📌 Copy of AI Engineer (Toronto)
🏢 PureFacts Financial Solutions
📍 Toronto