About this role
Rengo AI is building the intelligence layer for fund management — starting with next-generation portfolio monitoring systems for investment teams. Today, portfolio monitoring is fragmented across dashboards, spreadsheets, internal tools, and manual analyst workflows. Rengo replaces this with an AI-native monitoring layer that continuously interprets portfolio activity, risk, exposure, and performance across assets and strategies . The Role As a Founding AI Engineer , you will build the core system that powers AI-driven portfolio monitoring for institutional investors . You will design systems that continuously: ingest portfolio + market + position-level data detect meaningful changes and anomalies generate structured investment insights explain performance and risk drivers in natural language + structured outputs This is a high-reliability AI system , not a chatbot. What You’ll Build 1. AI Portfolio Monitoring Engine Real-time and batch systems that monitor: portfolio performance (PnL, attribution, drawdowns) exposure shifts (sector, geography, asset class) risk signals (volatility, correlation, concentration) position-level changes AI layer that converts raw portfolio data into: alerts summaries explanations actionable insights 2. Change Detection & Intelligence Layer Build systems that detect: significant portfolio movements abnormal price/volume behavior in holdings drift from target allocations risk regime changes Prioritization layer: what matters vs noise 3. AI-Generated Portfolio Narratives Generate structured outputs such as: daily / weekly portfolio reports performance explanations (“why did we lose/gain?”) exposure breakdowns risk commentary Ensure outputs are: auditable grounded in data consistent across runs 4. Data + Retrieval Systems for Funds Integrate: positions & holdings data market data feeds internal fund metadata external news & filings (optional enrichment layer) Build RAG pipelines over portfolio + market context 5. LLM Systems for Financial Reliability Design LLM pipelines that: avoid hallucinated financial reasoning produce structured, verifiable outputs ground insights in actual portfolio data Build evaluation frameworks for correctness of financial narratives