The RoleAs 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 Build1. 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 12 intelligence 12 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)
