Algorithmic Trading Developer

About Us

Innovation Fintech Co., Limited is a leading algorithmic trading firm specializing in developing cutting-edge trading strategies and systems, our team is composed of talented individuals who are passionate about technology, finance, and innovation.

Job Description

We are seeking a talented AI Engineer to join our dynamic team. The ideal candidate brings a strong software engineering foundation in Python, practical experience in model architecture and AI application development, and a keen interest in financial markets. In this role, you will leverage cutting-edge AI techniques to build scalable, high-performance solutions for our trading systems.

Key Responsibilities

• AI & Trading Systems: Design, develop, and maintain high-performance trading infrastructure and AI-driven trading systems, tools, or data pipelines.
• Quant & AI Collaboration: Collaborate closely with quantitative researchers and data scientists to implement, optimize, and productionize trading algorithms and AI/ML models.
• Engineering Excellence: Architect robust, scalable, and low-latency code for real-time processing, model inference, and trading execution in live financial environments.
• LLM & Agent Workflows: Integrate modern AI capabilities (e.g., LLMs, RAG, multi-agent systems, document processing) into quantitative analysis, market sentiment tracking, or execution workflows.
• Full Lifecycle Ownership: Participate in the complete software and AI development lifecycle—from requirement analysis, model/system architecture design, to testing, deployment, and MLOps/DevOps monitoring.
• Best Practices: Maintain strict version control, code review standards, and CI/CD pipelines using Git/GitHub.
• Performance Optimization: Continuously monitor and optimize system latency, throughput, model inference speed, and infrastructure reliability.
• Innovation & Research: Stay up to date with cutting-edge developments in both financial technology and artificial intelligence (e.g., new model architectures, AI frameworks) to keep our systems competitive.

Job Requirements

• Education: Bachelor’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related technical field.
• Experience: Minimum of 3 years of professional experience in software engineering, with a focused background in AI engineering, LLM application development, or machine learning systems.
• Programming & Tech Stack: Proficiency in Python and working knowledge of Java; hands-on experience with modern AI frameworks and orchestration tools (e.g., PyTorch, LangChain, LlamaIndex).
• AI & Architecture: Experience with AI model architecture and design, including practical expertise in RAG (Retrieval-Augmented Generation), model fine-tuning, prompt engineering, or LLM deployment/inference optimization.
• Core Fundamentals: Strong understanding of data structures, algorithms, and software design principles/patterns.
• Domain Knowledge (Plus): Basic understanding of the stock market, financial instruments, or financial domain concepts is a plus.
• Problem Solving: Excellent problem-solving skills, analytical thinking, and strong attention to detail.
• Adaptability & Collaboration: Ability to work independently and collaboratively in a fast-paced environment.
• Language Skills: Strong written and verbal communication skills in both English and Chinese.

Preferred Qualifications

• Domain & Trading: Experience in algorithmic trading, quantitative strategies, or AI/ML applications in financial markets.
• Tech Stack: Proficiency in SQL and database design; familiarity with low-latency or backend languages such as C++ or C#, or web/scripting tech like JavaScript/TypeScript.
• AI & Machine Learning: Practical experience with ML/DL frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and modern AI techniques like agentic workflows, vector databases, or embedding search.
• Cloud & Infrastructure: Hands-on experience with cloud computing platforms (AWS, GCP, or Azure), particularly with cloud AI services, containerization (Docker, Kubernetes), or ML infrastructure (MLOps)