Note: The job is a remote job and is open to candidates in USA. Merciv is building the intelligence layer for fund management with Rengo AI, focusing on next-generation portfolio monitoring systems for investment teams. As a Founding AI Engineer, you will develop core systems that provide AI-driven insights and monitoring for institutional investors.
Responsibilities
- 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
- Build 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
- Build systems that detect: significant portfolio movements, abnormal price/volume behavior in holdings, drift from target allocations, risk regime changes
- Generate structured outputs such as: daily / weekly portfolio reports, performance explanations (“why did we lose/gain?”), exposure breakdowns, risk commentary
- Integrate: positions & holdings data, market data feeds, internal fund metadata, external news & filings (optional enrichment layer)
- 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
Skills
- 3–7+ years in backend, data engineering, or ML systems
- Strong Python (mandatory)
- Experience building production data systems or analytics platforms
- Experience building LLM applications in production
- Strong understanding of RAG systems
- Strong understanding of structured generation (schemas, JSON outputs)
- Strong understanding of tool use / function calling
- Strong understanding of agent workflows
- Awareness of failure modes in LLM reasoning (critical in finance)
- Experience with time-series data
- Experience with event-driven pipelines
- Experience with analytics / observability systems
- Comfort working with imperfect, high-volume financial data
- Experience in asset management / hedge funds / fintech
- Experience in portfolio analytics or risk systems
- Experience in trading / market data infrastructure
- Familiarity with exposure/risk models
- Familiarity with PnL attribution systems
- Familiarity with BI / analytics platforms for finance
- Experience with vector databases or hybrid retrieval systems
Company Overview
Company H1B Sponsorship