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Job Description & How to Apply Below
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.
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 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
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
Lang Chain, Llama Index, 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 (Lang Graph or similar)
Hands-on experience building LLM-based applications or AI agents
Experience in SaaS, fintech, or data-driven environments is preferred
AI & Agent Expertise 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
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 Deg
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