AI Engineer

This is a fully remote position, open to applicants in New York.

📋 Description

• Develop the foundational system that facilitates AI-powered portfolio monitoring for institutional investors.

• Create systems that consistently: gather portfolio, market, and position-level data; identify significant changes and anomalies; produce structured investment insights; articulate performance and risk factors in both natural language and structured formats.

• AI Portfolio Monitoring Engine: Real-time and batch systems that track: portfolio performance (PnL, attribution, drawdowns); shifts in exposure (sector, geography, asset class); risk indicators (volatility, correlation, concentration); changes at the position level. This AI layer transforms raw portfolio information into: alerts, summaries, explanations, and actionable insights.

• Change Detection & Intelligence Layer: Develop systems that recognize: substantial portfolio shifts; unusual price/volume activity in holdings; deviations from target allocations; changes in risk regimes. Implement a prioritization layer to distinguish between significant information and noise.

• AI-Generated Portfolio Narratives: Produce structured outputs such as: daily and weekly portfolio reports; explanations of performance (“why did we lose/gain?”); breakdowns of exposure; commentary on risk. Ensure that outputs are: auditable, data-driven, and consistent across iterations.

• Data + Retrieval Systems for Funds: Integrate: positions and holdings data; market data feeds; internal fund metadata; external news and filings (optional enrichment layer). Construct RAG pipelines that utilize portfolio and market context.

• LLM Systems for Financial Reliability: Design LLM pipelines that: prevent hallucinated financial reasoning; yield structured, verifiable outputs; anchor insights in actual portfolio data. Establish evaluation frameworks to assess the accuracy of financial narratives.

⛳️ Requirements

• Strong engineering foundation.

• 3–7+ years of experience in backend, data engineering, or ML systems.

• Proficient in Python (required).

• Experience in developing production data systems or analytics platforms.

• Familiarity with LLM / AI systems.

• Proven experience in building LLM applications for production.

• Strong grasp of: RAG systems, structured generation (schemas, JSON outputs), tool usage/function calling, and agent workflows.

• Awareness of potential failure modes in LLM reasoning (critical in a financial context).

• Mindset geared towards data-intensive systems.

• Experience with: time-series data, event-driven pipelines, and analytics/observability systems.

• Ability to work with imperfect, high-volume financial data.

🏝️ Benefits

• Competitive salary.

• Flexible working hours.

• Professional development budget.

• Home office setup allowance.

• Global team events.