Lead Quantitative Researcher / Quant Engineer (AI-Focused)

The Role As a Quantitative Researcher / Quant Engineer (AI-Focused), you will design, test, and deploy AI-driven quantitative models that enable our agents to predict about markets, manage risk, and optimize outcomes. You’ll work at the frontier of AI x Finance, bridging statistical modeling, agentic reasoning, and data engineering to create adaptive, data-informed intelligence. Key Responsibilities • Quantitative Modeling & Research • Develop quantitative models for financial forecasting, strategy optimization, and portfolio analytics. • Incorporate LLM-based reasoning into quantitative pipelines to augment decision-making and contextual understanding. • Research market microstructure, trading patterns, and fund flow dynamics relevant to agent-driven financial systems. • Design and backtest AI-augmented trading or fundraising strategies using real and synthetic data. • AI Integration • Integrate LLMs and autonomous agents with quantitative models for dynamic hypothesis generation and adaptive strategy refinement. • Use multi-agent frameworks (e.g., LangGraph, CrewAI, AutoGen) to simulate collaborative decision-making between quant agents. • Explore reinforcement learning and LLM-based policy learning for optimization under uncertainty. • Data Engineering & Infrastructure • Build scalable data pipelines for market, sentiment, and alternative datasets using Python, PySpark, or SQL. • Deploy models in cloud-native environments (AWS, GCP) using Docker/Kubernetes. • Evaluation & Governance • Establish robust evaluation frameworks for agent-driven trading, forecasting, and decision systems. • Monitor model performance, bias, and explainability, ensuring alignment with regulatory and ethical standards. • Collaborate with AI and compliance teams to design transparent, auditable quant processes. ________________________________________ Qualifications • 5+ years in quantitative research, trading, or applied data science. • Deep understanding of statistical modeling, time-series analysis, and optimization techniques. • Proficiency in Python (NumPy, Pandas, PyTorch, or TensorFlow); experience with TypeScript is a plus. • Familiarity with LLM frameworks (LangChain, AutoGen, CrewAI) or AI-driven agentic systems. • Strong background in mathematical reasoning, probability theory, and stochastic processes. • Ability to translate research insights into production-grade systems. ________________________________________ Preferred Qualifications • Advanced degree (MS/PhD) in Applied Mathematics, Computer Science, Statistics, or Quantitative Finance. • Experience with reinforcement learning, meta-learning, or agentic decision frameworks. • Background in crypto markets, DeFi, or alternative asset analytics. • Contributions to open-source AI or quant research projects. • Experience integrating AI models into live financial systems or strategy engines. Apply tot his job