Rengo AI - AI Engineer

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