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.
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.
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
3–7+ years in backend, data engineering, or ML systems
Strong Python (mandatory)
Experience building production data systems or analytics platforms
Experience building LLM applications in production
Strong understanding of:
RAG systems
structured generation (schemas, JSON outputs)
tool use / function calling
agent workflows
Awareness of failure modes in LLM reasoning (critical in finance)
Experience with:
time-series data
event-driven pipelines
analytics / observability systems
Comfort working with imperfect, high-volume financial data
Experience in:
asset management / hedge funds / fintech
portfolio analytics or risk systems
trading / market data infrastructure
Familiarity with:
exposure/risk models
PnL attribution systems
BI / analytics platforms for finance
Experience with vector databases or hybrid retrieval systems
You are building the core monitoring brain of a fund
Not dashboards — interpretation + intelligence
Systems you build directly influence investment decisions and risk awareness
High emphasis on:
correctness
traceability
reliability under uncertainty
You own the full stack: data → intelligence → insight delivery
Python (core systems + AI orchestration)
LLM APIs (OpenAI / Anthropic / open-source models)
Postgres + time-series storage
Vector DB for semantic retrieval
Stream/batch processing pipelines
Cloud infrastructure (AWS/GCP)
Define how AI monitors institutional portfolios
Replace manual analyst workflows with automated intelligence systems
Work on one of the hardest AI problems in finance: turning data into trustworthy interpretation
High ownership, early-stage, no legacy constraints