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- Tasks: Lead the evolution of trading infrastructure using AI and automation.
- Company: Dynamic financial institution focused on innovation and technology.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Why this job: Make a real impact in trading operations while leveraging cutting-edge AI technologies.
- Qualifications: 5+ years in finance, strong Python and SQL skills, and a passion for problem-solving.
- Other info: Fast-paced environment with potential for significant career advancement.
The predicted salary is between 48000 - 72000 £ per year.
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 P&L visualization tools.
Key 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.
- 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: 5+ 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: Deep 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 (e.g. 60-70/week) to meet global trading demands.
- Agility: Willingness to participate in a rotational weekend and 24/7 on-call schedule, providing immediate resolution to live production issues.
Trading Ops & Risk Systems Engineer (AI & Automation) employer: Medium
Contact Detail:
Medium Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Trading Ops & Risk Systems Engineer (AI & Automation)
✨Tip Number 1
Network like a pro! Reach out to folks in the trading and risk management space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI workflows or any projects you've done with Python and machine learning. This is your chance to shine and demonstrate your technical prowess.
✨Tip Number 3
Prepare for interviews by brushing up on your financial acumen. Be ready to discuss P&L attribution and market risk indicators. We want to see that you can talk the talk as well as walk the walk!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Trading Ops & Risk Systems Engineer (AI & Automation)
Some tips for your application 🫡
Show Off Your Technical Skills: Make sure to highlight your experience with Python and SQL in your application. We want to see how you've used these skills in real-world scenarios, especially in trading ops or risk management.
Demonstrate Your Analytical Mindset: We love candidates who can think critically! Share examples of how you've tackled complex problems in the past, particularly those involving P&L analysis or risk monitoring. This will show us you're a great fit for our analytical culture.
Tailor Your Application: Don’t just send a generic CV! Tailor your application to reflect the specific requirements of the Trading Ops & Risk Systems Engineer role. Mention your experience with AI workflows and how you’ve automated processes before.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at Medium
✨Know Your Tech Stack
Make sure you’re well-versed in Python and SQL, as these are crucial for the role. Brush up on your knowledge of AI/ML frameworks like OpenAI API and LangChain, and be ready to discuss how you've used them in past projects.
✨Demonstrate Your Analytical Skills
Prepare to showcase your analytical prowess by discussing specific examples of P&L attribution and risk management. Be ready to explain how you’ve decomposed market moves and used data to inform decision-making.
✨Showcase Your Problem-Solving Mindset
Highlight your systems-thinking approach by sharing instances where you identified manual processes that needed automation. Discuss how you tackled these challenges and the impact it had on efficiency.
✨Be Ready for High-Pressure Scenarios
Since this role demands agility in a fast-paced environment, prepare for questions about how you handle stress and tight deadlines. Share experiences where you thrived under pressure, especially in trading or operational contexts.
