A premier quantitative trading firm operating across global financial markets. We combine cutting-edge technology, sophisticated data infrastructure, and advanced AI capabilities to drive our trading and research operations.

We are looking for a Python / AI Platform Engineer to join our backend engineering team—someone who can build robust trading infrastructure while spearheading the adoption of AI-powered automation across the firm.

The Role:

This is a hybrid role that sits at the intersection of data engineering, backend systems, and AI application development. You will be responsible for the systems that power our trading, research, and operational workflows—while driving the engineering implementation of LLM-based tools to enhance intelligence and automation firm-wide.

What You'll Actually Do:

AI & LLM Engineering (Key Focus):

1. Build Intelligent Agents: Design and implement internal AI agents and multi-step automation systems powered by LLMs (OpenAI, Claude, Qwen, or similar).
2. Orchestrate Complex Workflows: Develop task orchestration frameworks that combine tool calling, automated execution, and decision-making logic.
3. Deploy RAG Systems: Build Retrieval-Augmented Generation pipelines for internal knowledge retrieval—supporting research queries, data lookups, and compliance Q&A scenarios.
4. Own the LLM Stack: Manage the full engineering lifecycle—prompt management, embedding pipelines, vector databases, model API integrations, and structured output generation.

Backend & Data Engineering:

1. Design Core Systems: Develop and maintain Python backend services that power trading, risk monitoring, reconciliation, and business process automation.
2. Build Reliable Data Pipelines: Engineer high-performance ETL/ELT workflows and job scheduling systems to ensure market and trading data flow accurately, efficiently, and traceably across all platforms.
3. Optimize Databases: Work with SQL and NoSQL databases (MySQL, PostgreSQL, etc.) to support high-concurrency and large-scale data access.
4. Create Visibility Tools: Build automated reporting, monitoring dashboards, and analytics systems to improve operational transparency and decision-making.
5. Troubleshoot & Resolve: Investigate and fix production data anomalies, system failures, and performance bottlenecks to ensure stability and accuracy.

Who You Are (Must-Haves):

Core Engineering Skills:

1. Python Mastery: Solid backend development experience with Python. You write clean, maintainable, production-grade code.
2. Database Proficiency: Strong SQL skills and experience designing schemas for complex data processing scenarios.
3. Linux & Scripting: Comfortable in Linux environments with Shell scripting proficiency.
4. Pipeline Experience: Proven track record building data pipelines, workflow schedulers, or ETL systems.
5. System Design Mindset: You think about architecture, scalability, and reliability before writing a single line of code.

AI / LLM Experience (Critical):

1. LLM Application Development: Hands-on experience building applications using LLM APIs (OpenAI, Claude, Qwen, or equivalents).
2. Prompt Engineering: Skilled in designing prompts and structured outputs for reliable, production-ready results.
3. Agent Systems: Experience developing tool calling, function calling, or multi-agent architectures.
4. RAG Knowledge: Familiarity with embedding pipelines, vector search, and retrieval-augmented generation systems.
5. Workflow Automation: You have designed or implemented automated AI workflows involving multiple steps and orchestration logic.

Soft Skills:

1. Independent & Fast-Paced: You thrive in complex, dynamic environments and can drive projects forward with minimal supervision.
2. Analytical Problem-Solver: You break down complex problems into actionable engineering solutions.
3. Clear Communicator: You can explain technical concepts to non-technical stakeholders and collaborate across teams.
4. Automation-Obsessed: You are passionate about improving system efficiency through intelligent automation.
5. Bilingual: Fluent in English and Chinese (Mandarin or Cantonese).

Why You Should Consider This:

1. Greenfield AI Mandate: This is not a support role. You are building AI capabilities from the ground up.
2. Real-World Impact: Your systems will directly influence trading decisions, risk management, and operational efficiency.
3. Cutting-Edge Tech Stack: Work with modern AI frameworks, LLMs, vector databases, and high-performance data infrastructure.
4. High-Caliber Team: Collaborate with quants, traders, and engineers who are leaders in their fields.
5. Competitive Compensation: Attractive package commensurate with experience and impact.