Quantitative Developer

Overview Quantitative Developer - AI Implementation at WorldQuant. This role focuses on developing and maintaining components of an AI-driven platform that integrates Large Language Models (LLMs) with WorldQuant's strategy management tools to provide AI-assisted insights, automate routine analytical tasks, and enable natural language interactions for Portfolio Managers. Project Scope This is a cutting-edge initiative to integrate Large Language Models (LLMs) with WorldQuant's proprietary strategy management tools. The project aims to provide AI-assisted insights, automate routine analytical tasks, and enable natural language interactions for Portfolio Managers to more efficiently analyze, maintain, and enhance their trading strategies. This involves building foundational infrastructure, developing robust data pipelines for metrics and features, and creating a secure, user-centric interface with a strong focus on protecting intellectual property. Role Summary We are seeking a talented and motivated Full-Time Software Engineer to play a key role in the development and enhancement of this AI-driven platform. You will be responsible for designing, building, and maintaining critical components of the system, primarily focusing on the Model Context Protocol (MCP) servers that form the backbone of the platform. This includes enhancing existing MCPs, such as our proprietary command-line interface for strategy data, developing new MCPs for internal data system integration, strategy analysis, and performance metrics, and contributing to the secure and effective use of LLMs. Key Responsibilities Design, develop, test, and deploy robust and scalable MCP servers and related tools in Python. Enhance existing MCPs, including our internal data access tools, to ensure reliable access to strategy metadata, historical PnL data, and PnL attribution by risk factors. Develop new MCPs, including but not limited to MCPs for direct data system integration, MCPs for strategy analytics, and MCPs for performance reporting. Design and implement secure prompt engineering techniques to interact with LLMs, ensuring effective information retrieval while minimizing the risk of intellectual property leakage. Collaborate with project managers, technical leads, and other developers to define technical specifications and integrate various data sources. Work on API development and integration, ensuring seamless data flow between different systems (internal knowledge management systems, simulation systems, data warehouses). Implement data parsing and manipulation logic for various formats (JSON, CSV). Contribute to the design and implementation of data pipelines for strategy metrics and features. Address and resolve bugs and issues in existing MCP tools and infrastructure. Participate in code reviews, and contribute to unit and integration testing efforts. Create and maintain technical documentation for developed components. Stay updated with emerging technologies and methodologies in AI, LLMs, prompt engineering, and financial technology. Qualifications Core Skills: Strong proficiency in Python programming; proven experience in API development, integration, and consumption; solid understanding of database interaction, data access patterns, and data modeling; experience with data parsing and manipulation libraries (e.g., pandas, JSON, CSV). Understanding of or experience with prompt engineering principles for Large Language Models. Awareness of data security and intellectual property protection considerations in the context of AI and LLM applications. Domain-Specific Knowledge (Highly Beneficial): Familiarity with financial data (e.g., PnL, Sharpe ratio, alpha, risk factors, AUM); experience with quantitative trading systems, market data systems, or strategy management platforms (familiarity with internal data systems or command-line data tools is a significant plus). Technical Environment Familiarity (Helpful): Experience working in a Linux environment; understanding of CLI tools and development; familiarity with version control systems (e.g., Git). Soft Skills: Excellent problem-solving and analytical skills; strong collaboration and communication skills; high attention to detail; ability to learn quickly and adapt to new technologies and complex systems. Education: Bachelor\'s or Master\'s degree in Computer Science, Engineering. Our Benefits Core Benefits: Fully paid medical and dental insurance for employees and dependents, flexible spending