# Rengo AI - AI Engineer at deCircle

> Remote · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/recruitee%3Adecircletalentpartner%3A2610047 (read 2026-11-08).

## Facts (observed)

- Company: [deCircle](https://alderhq.app/companies/decircle)
- Location text: New York, New York, United States
- Where it can be done from (inferred): Remote · United States (evidence: "Location: New York, New York, United States")
- Posted: 2026-05-20 (172 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://talent.makerdao.network/o/rengo-ai-ai-engineer
- Skills listed (inferred): Python, PostgreSQL, AWS, GCP, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (New York, n 90): p25 $177k, median $233k, p75 $275k

## Description (the employer's text)

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

### Requirements

### 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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/recruitee%3Adecircletalentpartner%3A2610047 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.