D
Rengo AI - AI Engineer
De Circle
1 hour ago
Full-time
On-site
East New York, New York, United States
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 Strong engineering background
3-7+ years in backend, data engineering, or ML systems Strong Python (mandatory) Experience building production data systems or analytics platforms LLM / AI systems experience 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) Data-heavy systems mindset Experience with: time-series data event-driven pipelines analytics / observability systems
Comfort working with imperfect, high-volume financial data Nice to Have 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 What Makes This Role Unique
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 Tech Direction
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) Why Join
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
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 Strong engineering background
3-7+ years in backend, data engineering, or ML systems Strong Python (mandatory) Experience building production data systems or analytics platforms LLM / AI systems experience 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) Data-heavy systems mindset Experience with: time-series data event-driven pipelines analytics / observability systems
Comfort working with imperfect, high-volume financial data Nice to Have 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 What Makes This Role Unique
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 Tech Direction
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) Why Join
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