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

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