PureFacts is the leader in the Revenue Performance Management category for wealth and asset management firms. The PureRevenue™ Platform helps organizations maximize revenue potential by connecting pricing, billing, compensation, advisor behavior, and AI-powered intelligence within a single Revenue Book of Record. By transforming fragmented revenue processes into a coordinated growth system, firms gain greater visibility, stronger pricing discipline, improved revenue capture, and more effective advisor alignment. The result is faster organic growth, improved profitability, and increased enterprise value. For more than 25 years, PureFacts has helped leading financial institutions turn revenue from an operational process into a strategic advantage. 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. 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 . About the role

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 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: 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 transparent, practical way Qualifications

2+ years building production, customer-facing LLM/GenAI applications, including vector databases, RAG pipelines , agent orchestration 6-8+ years of total back-end software engineering experience Deep, hands‑on experience building with agent frameworks (e.g., Microsoft Agent Framework, Google ADK, LangGraph, etc.), including designing custom orchestration patterns beyond out‑of‑the‑box templates Demonstrated ownership of evaluation frameworks and pipelines and design of deterministic guardrails/safety controls in regulated or compliance‑sensitive contexts Experience in SaaS, fintech, or other data‑driven, regulated environments strongly preferred Technical Skills

Expert‑level Python (required) Deep experience with: LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.), including cost/latency tradeoffs at scale API and microservices architecture, including integrating LLM and ML model components into larger systems Data processing (SQL, Python data libraries) and pipeline design for both retrieval-quality data and model training data

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