Ai Engineer

Role: Building & Owning the AI Agent Infrastructure Designing and deploying multi-agent systems for investment research, M&A due diligence screening, and FP&A automation using Python and frameworks like LangChain or CrewAI Setting up and managing a private cloud environment (AWS or Azure) where all AI workloads run ensuring client data never leaves Tristones controlled environment Building and maintaining an internal knowledge base (vector database) that indexes all past project work and makes it searchable by AI agents and analysts Integrating AI agents with external financial data sources using controlled API connections with rate limiting and audit logging Owning the prompt library: write, test, version-control, and continuously improve the prompts used by all agents Data Privacy & Security Architect a private LLM deployment self-hosted open-source models or private cloud instances so client data never passes through shared public APIs Implementing data classification: tag incoming ...