Build and integrate LLM-powered features into full-stack investment applications. Develop RAG systems, agentic workflows, prompt-engineered pipelines, data services, and evaluation frameworks under the guidance of a Senior AI Engineer. Translate investment and operations needs into technical solutions, write testable code, measure model and retrieval quality, and contribute to production AI workflows. This hybrid Atlanta role requires strong Node.js and TypeScript fundamentals, practical LLM experience, clear communication, and rapid learning.

Support product development across internal CRM and fund/investor/advisor platforms. Maintain and prioritize backlog, write user stories and release notes, triage Jira Service Desk, perform QA, participate in sprints, manage multi-system requirements, gather stakeholder feedback, and support documentation and small-to-medium feature delivery.

Own AI feature delivery across multiple internal platforms: write detailed AI-native user stories and acceptance criteria, prioritize backlog, lead grooming and sprint planning, perform acceptance testing/QA, triage AI service-desk requests within SLA, liaise with engineering and senior stakeholders, and produce documentation and process flows to support deployment and adoption.