AI Engineer
Founding AI Engineer building an AI-native portfolio monitoring system for institutional investors, designing data ingestion pipelines, real-time analytics, and machine learning models to interpret portfolio activity, risk, exposure, and performance across assets and strategies.
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
Posted June 19, 2026