Quant trader

My client is seeking a highly sophisticated Senior Quantitative Trader with a proven track record of profitability to lead a groundbreaking initiative: translating human trading expertise, market intuition, and quantitative strategies into autonomous AI agents.

In this role, you will not just execute trades; you will act as the "Domain Expert and Principal Trainer" for our proprietary machine learning and LLM-driven trading agents. You will systematically deconstruct your own edge, decision-making frameworks, and risk management strategies to design the reward functions, feature sets, and environment parameters that will train the next generation of AI quant traders.

1. Strategy Deconstruction & Modeling: Systematically break down your proprietary trading alpha, execution tactics, and market intuition into structured rules, heuristics, and mathematical frameworks that data scientists can model.
2. Agent Training & Reinforcement Learning (RL): Collaborate closely with AI/ML engineers to design reward functions, state spaces, and action spaces for reinforcement learning agents, ensuring they align with profitable trading behaviors.
3. Prompt Engineering & Knowledge Base Curation: Build, curate, and maintain the "expert knowledge base" (via RAG and advanced prompting) that LLM-based trading agents use to interpret market regime shifts, news, and macroeconomic events.
4. Simulation & Adversarial Testing: Design rigorous historical simulation environments and adversarial scenarios to stress-test AI agents, identifying edge cases where agent logic breaks down compared to human intuition.
5. Live Guardrails & Monitoring: Define the risk parameters, stop-losses, and operational guardrails under which the autonomous agents will operate in live markets, and oversee their initial deployment.
6. Continuous Feedback Loop: Analyze agent performance discrepancies versus human execution, diagnosing why an agent missed an opportunity or mismanaged risk, and continuously fine-tune the training pipeline.

1. Trading Track Record: 5+ years of experience as a Quantitative Trader or Portfolio Manager with a verifiable, consistent track record of generating alpha (Sharpe > 2.0 preferred) in Liquid Markets (Crypto, FX, Equities, or Futures).
2. Technical Proficiency: Strong programming skills in Python and familiarity with quantitative libraries (Pandas, NumPy, SciPy).
3. Conceptual ML Knowledge: Deep conceptual understanding of Reinforcement Learning (RL), Deep Learning, and Large Language Models (LLMs). You don't need to write the neural networks from scratch, but you must know how they learn.
4. Market Microstructure Expertise: Deep understanding of order book dynamics, liquidity provision, execution algorithms (TWAP/VWAP), and market impact modeling.
5. Education: Master’s or Ph.D. in a highly quantitative field (Computer Science, Mathematics, Physics, Quantitative Finance, or Engineering).

Fluency in Mandarin is a MUST for the role.