Copy of AI Engineer

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 Pure Facts' AI-first strategy, developing AI-powered copilots, agents, and automation tools that reduce manual work, improve productivity, and deliver meaningful client value.

• 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

• 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

• Integrate LLMs into Pure Facts’ 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

• 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

• Develop frameworks to evaluate LLM outputs for quality, accuracy, and reliability
• Continuously optimize prompts, models, and workflows
• Monitor system performance and implement improvements

• Leverage and integrate tools such as:
• OpenAI, Azure OpenAI, or similar LLM providers
• Lang Chain, Llama Index, or agent frameworks
• APIs, microservices, and cloud infrastructure
• Collaborate with MLOps to ensure scalable and maintainable deployments

• Ensure AI solutions are secure, compliant, and aligned with responsible AI principles
• Address:
• Data privacy and security
• Model hallucination and reliability
• Explainability and transparency

• 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

• 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 (Lang Graph or similar)
• Hands-on experience building LLM-based applications or AI agents
• Experience in SaaS, fintech, or data-driven environments is preferred

• Experience building:
• Retrieval-Augmented Generation (RAG) systems
• Multi-step agent workflows
• Tool-using agents and automation systems
• Strong understanding of:
• LLM limitations and optimization techniques
• Evaluation methods for generative AI

• 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

• Ability to work across technical and non-technical teams
• Strong problem-solving and systems thinking skills
• Clear communication of complex AI concepts

• Deg