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:

• 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

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