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:

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ingest portfolio + market + position-level data

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detect meaningful changes and anomalies

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generate structured investment insights

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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

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Real-time and batch systems that monitor:

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portfolio performance (PnL, attribution, drawdowns)

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exposure shifts (sector, geography, asset class)

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risk signals (volatility, correlation, concentration)

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position-level changes

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AI layer that converts raw portfolio data into:

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alerts

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summaries

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explanations

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actionable insights

2. Change Detection & Intelligence Layer

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Build systems that detect:

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significant portfolio movements

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abnormal price/volume behavior in holdings

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drift from target allocations

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risk regime changes

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Prioritization layer: what matters vs noise

3. AI-Generated Portfolio Narratives

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Generate structured outputs such as:

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daily / weekly portfolio reports

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performance explanations (“why did we lose/gain?”)

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exposure breakdowns

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risk commentary

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Ensure outputs are:

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auditable

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grounded in data

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consistent across runs

4. Data + Retrieval Systems for Funds

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Integrate:

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positions & holdings data

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market data feeds

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internal fund metadata

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external news & filings (optional enrichment layer)

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Build RAG pipelines over portfolio + market context

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5. LLM Systems for Financial Reliability

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Design LLM pipelines that:

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avoid hallucinated financial reasoning

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produce structured, verifiable outputs

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ground insights in actual portfolio data

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Build evaluation frameworks for correctness of financial narratives