Key Job Duties

Strategy Engine & Backtesting Infrastructure : Architect and optimize a high-fidelity, event-driven backtesting engine capable of validating AI-generated strategies against historical data, accounting for slippage, latency, and fees.

MCP Server Implementation: Develop and maintain Model Context Protocol (MCP) servers to acquire, normalize, and stream real-time data from disparate sources into the AI strategy engine.

Smart Execution Algorithms: Design and implement low-latency execution logic (e.g., TWAP, VWAP, Smart Order Routing) to minimize market impact and optimize fill rates for user-deployed strategies across CeFi and DeFi venues.

Real-Time Risk Management System: Build a robust pre-trade risk engine that monitors live positions, calculating real-time metric and enforcing hard limits to protect user capital.

High-Performance Pipeline Optimization: Streamline the deployment of quantitative models into production, ensuring seamless communication between the Python-based AI agent and the execution layer.

Exchange Connectivity: Maintain robust WebSocket and REST API connections with major exchanges, handling rate limits, disconnects, and data integrity issues automatically.

Requirements

Bachelor in a quantitative discipline such as Computer Science, Mathematics, Physics, Financial Engineering, or a related field with relevant coursework.

1+ years of experience in quantitative development, algorithmic trading, or high-performance software engineering.

Excellent in Python (Pandas, NumPy, Asyncio) for data processing

Experience building real-time data pipelines and working with time-series databases

Ability to translate complex mathematical logic (e.g., risk models, technical indicators) into efficient, production-readycode.