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 explicit, 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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