AI Engineer, Trading Ops & Management

Key Responsibilities

1. Trading Operations and Performance Analysis with AI

• Manage the end-to-end lifecycle of trades across multi-asset classes. Develop agentic AI workflows using Python (ML) and MLOps tools to automate manual operations and reconciliations

• Perform daily P&L attribution and explain variance by decomposing market moves, Greeks, and new activity. Leverage AI/ML frameworks (e.g., OpenAI API, LangChain, or local LLMs) to build intelligent agents for anomaly detection in trade data and automated commentary generation for P&L reports.

• Ensure data integrity across trading systems, middle-office platforms, and downstream finance ledgers.

• Use AI tools to build reporting and analyses on trading performance and perform adhoc business analytics

2. Risk Monitoring & Control

• Intelligent Risk Management: Develop real-time, AI-driven risk models that use machine learning to predict market volatility and alert the desk to emerging cross-market risks.

• Develop and maintain sophisticated risk reporting dashboards for the desk.

Requirements & Qualifications

• Experience: 3+ years in front-office operations, quant development, or a blend of risk management and systems architecture within a financial institution.

• Technical Stack: Expert proficiency in Python (for AI/ML), and strong SQL skills. Experience building agentic AI workflows is a significant advantage.

• Financial Acumen: Good understanding of P&L attribution, financial controls, market risk indicators (Greeks, VaR), financial markets and products.

• Problem Solving: A "systems-thinking" approach to operations—treating every manual task as a bug that needs an automated solution.

• Capacity: Ability to thrive in a high-intensity environment requiring extended hours when necessary to meet global trading demands.

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