Rengo AI - AI Engineer

About Rengo AI

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, and explain performance and risk drivers in natural language + structured outputs. This is a high-reliability AI system, not a chatbot.

What You’ll Build

• 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), and position-level changes. AI layer that converts raw portfolio data into alerts, summaries, explanations, and actionable insights.
• Change Detection & Intelligence Layer — Build systems that detect significant portfolio movements, abnormal price/volume behavior in holdings, drift from target allocations, and risk regime changes. Prioritization layer: what matters vs noise.
• AI-Generated Portfolio Narratives — Generate structured outputs such as daily/weekly portfolio reports, performance explanations (“why did we lose/gain?”), exposure breakdowns, and risk commentary. Ensure outputs are auditable, grounded in data, and consistent across runs.
• Data + Retrieval Systems for Funds — Integrate positions & holdings data, market data feeds, internal fund metadata, and external news & filings (optional enrichment layer). Build RAG pipelines over portfolio + market context.
• LLM Systems for Financial Reliability — Design LLM pipelines that avoid hallucinated financial reasoning, produce structured, verifiable outputs, and ground insights in actual portfolio data. Build evaluation frameworks for correctness of financial narratives.