Structurer, Complex Structuring / Systematic — Vice President

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

This is a front‑office, builder's seat at the intersection of systematic portfolio management, structuring and applied AI engineering. The strategy logic underpinning our organization is established; the opportunity is in the engineering, infrastructure, and tooling that surrounds it. You will own the data and execution infrastructure end‑to‑end, design and ship AI‑assisted internal tooling that compresses discretionary execution decisions, and serve as the named human‑in‑the‑loop for the residual execution decisions the algorithm does not fully automate.

This role is for a pragmatic engineer who wants proximity to live markets, genuine system ownership, and a mandate that is explicitly AI‑forward — backed by the stability and platform of a global financial institution.

Key Responsibilities

• Data & Execution Infrastructure
  • Own, harden, and right‑size the data infrastructure underpinning the systematic trading book, prioritising efficiency and reliability over unnecessary scale.
  • Migrate and modernise legacy components into a maintainable, testable, and auditable codebase.
  • Operate effectively within a firewalled, controlled environment (e.g. standard‑library and approved‑package Python without open external dependency installation).
• AI‑Assisted Tooling
  • Design, build, and ship LLM‑backed internal tooling that surfaces decision‑relevant context — market events, related‑instrument moves, order‑book state — to reduce latency in discretionary execution.
  • Deliver production‑grade AI applications (RAG pipelines, agentic workflows, evaluation frameworks) that are actively used, not experimental prototypes.
• Systematic Execution & Oversight
  • Exercise and supervise the subset of execution decisions (~10%) that fall outside pure algorithmic rules, under your SFC Type 9 licence.
  • Conduct pre‑trade and post‑trade scenario analysis, including payoff risk and P&L impact assessments.
  • Support customised back‑tests and client portfolio analysis as required.
• Product & Structuring
  • Contribute to the development of new products and payoff structures, working in close partnership with institutional sales on pre‑trade and post‑sales activity.
  • Support marketing presentations and participate in client meetings as the technical structuring voice where relevant.
• Governance & Risk
  • Partner with control functions — Legal, Compliance, Market Risk, Credit Risk, Audit, and Finance — to ensure appropriate governance and control infrastructure is maintained.
  • Appropriately assess the risk/reward of transactions and infrastructure decisions, demonstrating sound judgment and consideration for the firm's reputation.
  • Adhere to Citi's Code of Conduct, the Plan of Supervision for Global Markets and Securities Services, and all applicable policies and procedures.
  • Obtain and maintain all registrations and licences required for the role within the agreed timeframe.
  • Escalate, manage, and report control issues with transparency; safeguard Citigroup, its clients, and its assets by driving compliance with applicable laws, rules, and regulations.

Required Qualifications

• Strong data‑engineering craft with demonstrated judgment to right‑size solutions — you identify the simplest tool that solves the problem, not the most impressive one.
• Proven delivery in constrained environments: demonstrated ability to ship in firewalled or dependency‑restricted settings (e.g. standard‑library‑only Python).
• Production AI tooling: real‑world experience building and deploying internal LLM‑backed applications (RAG, agentic workflows, evaluation frameworks) that were actively adopted — not personal experiments or proofs of concept.
• Systematic trading execution fluency (ideal, not required): working knowledge of futures/multi‑asset execution, transaction cost analysis (TCA), and order routing — not merely familiarity with ML vocabulary.
• Ownership mindset: pragmatic, self‑directed, and comfortable as the primary engineer on a lean team.
• Ability to adapt and evolve in a regulated function to acquire experience spanning quantitative structuring, systematic trading engineering in a financial institution context.
• Demonstrated quantitative and analytical skills.
• Clear and concise written and verbal communication; ability to work across multiple functional groups (front office, risk, legal, compliance, technology).

Preferred Qualifications

• Statistical or signal‑research capability, with appetite to grow the role toward data science over time.
• Hands‑on experience migrating legacy VBA/Excel systems into modern, maintainable stacks.
• Prior experience in a structured products or derivatives environment.

Education

• Bachelor's degree required (quantitative discipline preferred — Computer Science, Engineering, Mathematics, Physics, or equivalent).
• Master's degree or higher preferred.

What This Role Offers

• Greenfield ownership of the systems that run a live systematic portfolio management book.
• A direct reporting line to a senior front‑office leader and an explicitly AI‑forward mandate.
• Front‑office placement with SFC regulatory standing (Type 9), sponsored by the firm.
• The institutional depth, platform, and stability of a global financial institution.

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