Founding AI Engineer

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, * 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

• 3-7+ years in backend, data engineering, or ML systems
• Strong Python (mandatory)
• Experience building production data systems or analytics platforms, + RAG systems
• structured generation (schemas, JSON outputs)
• tool use / function calling
• agent workflows
• Awareness of failure modes in LLM reasoning (critical in finance), + time-series data
• event-driven pipelines
• analytics / observability systems
• Comfort working with imperfect, high-volume financial data, + 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