Rengo AI - AI Engineer

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

1. 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

1. 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

1. 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

1. 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