AI Engineer

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🕒 vor 2 Monaten

🗽 New York – Remote

🗽 New York – Remote

⏰ Vollzeit

🟡 Mittelstufe

🟠 Senior

🤖 KI-Ingenieur

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1 - 10 Mitarbeiter

Gegründet 2019

📣 Marketing

📦 Logistik

💼 Beratung

Marketing • Logistics • Consulting

Wir arbeiten mit disruptiven Organisationen zusammen, um Talente zu identifizieren und anzuziehen. Als Boutique-Personalvermittlung konzentrieren wir uns auf Blockchain und Web3. Wir genießen es, mit Enthusiasten aus dem Web3- und Dezentralisierungsbereich zu netzwerken und ihnen zu helfen, ihren Platz in der dezentralen Welt zu finden.

Beschreibung

• Build the core system that powers AI-driven portfolio monitoring for institutional investors • 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 • 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 • 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 • 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 • 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 • 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

🎯 Anforderungen

• 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

🏖️ Vorteile

• Competitive salary • Flexible working hours • Professional development budget • Home office setup allowance • Global team events