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