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 #J-18808-Ljbffr deCircle