Vice President, AI-Driven Systematic Trading Platform

Citi; Hong Kong; On-site; HKD 1,500,000 - 2,300,000; Full time

Job summary

Citi Hong Kong is seeking a pragmatic engineer to own data and execution infrastructure for a live systematic trading framework. You will design and ship AI‑assisted internal tooling that surfaces decision context and you will act as the named human‑in‑the‑loop for residual execution decisions.

Responsibilities include modernising legacy components, building restricted‑Python environments, and ensuring governance with legal, risk, and compliance teams while partnering with front office to

Qualifications

• Strong data-engineering craft with judgment to right-size solutions.
• Proven delivery in constrained environments: Python with standard library only.
• Production AI tooling: real-world experience building and deploying internal LLM-backed applications that were actively adopted.
• Systematic trading execution fluency: knowledge of futures/multi-asset execution, TCA, and order routing.
• Ownership mindset: pragmatic, self-directed on a lean team.
• Ability to adapt in a regulated financial context spanning quantitative structuring and systematic trading engineering.

Responsibilities

• Own data and execution infrastructure end-to-end for the live systematic trading book.
• Design and ship AI-assisted internal tooling to surface decision context and reduce latency in discretionary execution.
• Serve as the named human-in-the-loop for residual decisions outside pure algorithmic rules.
• Migrate legacy components into maintainable, auditable code and ensure governance.
• Collaborate with control functions to maintain risk controls and regulatory compliance.

Skills

Data engineering

Restricted Python

AI tooling

Systematic trading

Ownership mindset

Communication

Regulatory context

Quantitative skills

Education

Bachelor's degree in a quantitative discipline

Master's degree or higher

Tools

Python

Pandas

SQL

Git

LLM tooling

Job description

Citi Hong Kong is seeking a pragmatic engineer to own data and execution infrastructure for a live systematic trading framework. You will design and ship AI‑assisted internal tooling that surfaces decision context and you will act as the named human‑in‑the‑loop for residual execution decisions.

Responsibilities include modernising legacy components, building restricted‑Python environments, and ensuring governance with legal, risk, and compliance teams while partnering with front office to