AI Engineer, Trading Ops & Management

We are seeking a highly analytical and technical professional to spearhead the evolution of our trading infrastructure. This hybrid role combines the fast-paced execution of a Trading Operations Specialist, the analytical rigor of a Risk Manager, and the technical capabilities of an AI-focused Developer.

The successful candidate will not only manage daily operational workflows and risk monitoring but will also lead the digital transformation of these functions. You will leverage Large Language Models (LLMs), machine learning, and advanced scripting to automate legacy manual processes, improve predictive risk modeling, and build real-time performance visualization tools and analyses.

1. Trading Operations and Performance Analysis with AI

Responsibilities

• 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

• 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.

Stack: Python

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