Role Overview
We are seeking an AI Quantitative Agent Developer to design, build, and iterate AI-powered modular agent capabilities (skills) for financial and quantitative systems.
This role focuses on AI-assisted development, rapid prototyping, and effective use of LLM-based tools. You will contribute to building intelligent systems that interpret structured instructions, interact with data pipelines, and support automated decision workflows in a controlled environment.
Key Responsibilities
1. AI Agent Development
- Design and develop modular agent components for financial data processing, trading workflows, and risk monitoring
- Build reusable components for AI-driven decision support systems
- Prototype and iterate on agent capabilities using AI-assisted development tools
2. LLM Integration & Tooling
- Evaluate and improve tool-calling performance of large language models (e.g., Qwen, MiniMax, or similar)
- Enhance model accuracy in handling structured financial and operational instructions.
- Contribute to prompt design, orchestration logic, and system reliability.
3. System Integration & Testing
- Integrate AI-generated components into quantitative system frameworks
- Conduct backtesting, simulations, and stress testing in local or distributed environments
- Support performance optimization and system reliability improvements
Requirements
- Proficiency in Python and the ability to review and refine AI-generated code
- Experience or strong interest in AI-assisted development tools (e.g., Cursor, Windsurf, or similar)
- Familiarity with LLM concepts such as tool calling, prompting, or agent frameworks
- Understanding of quantitative systems, trading logic, or data-driven automation is advantageous
- Ability to rapidly prototype and iterate from concept to working MVP
- Interest in local model deployment or GPU-based computing environments is a plus
Preferred Qualifications
- Experience with financial data pipelines, APIs, or trading systems
- Exposure to LLM frameworks, agent orchestration, or retrieval-based systems (RAG)
- Experience working with GPU-accelerated environments or local inference setups
- Knowledge of risk management or automated decision-making systems
