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. #J-18808-Ljbffr