Structurer, Complex Structuring / Systematic — Vice President

Citigroup Inc.; Hong Kong; On-site; HKD 1,200,000 - 1,800,000; Full time

Job summary

Citi Hong Kong is hiring a front-office engineer to own data and execution infrastructure for a systematic portfolio management framework. The role focuses on AI-assisted tooling, live market proximity, and end-to-end system ownership within a regulated, global financial institution.

You will build production-grade AI applications, manage data pipelines, and partner with front office, risk, and compliance to ensure governance.

Qualifications

• Strong data-engineering craft with pragmatic judgment to right-size solutions.
• Proven delivery in constrained environments (firewalled or dependency-restricted settings).
• Production AI tooling experience with real-world adoption of internal LLM-backed applications.
• Systematic trading execution fluency is ideal (futures/multi-asset, TCA, order routing).
• Ownership mindset: self-directed, primary engineer on a lean team.

Responsibilities

• Own, harden, and right-size the data and execution infrastructure for the systematic trading book.
• Migrate and modernise legacy components into a maintainable, testable codebase.
• Build and ship AI-assisted internal tooling with low latency for discretionary decisions.
• Provide governance and risk oversight with control functions and regulatory alignment.

Skills

Data engineering

Python

AI tooling

Regulatory awareness

Cross-functional collaboration

Quantitative analysis

Communication

Education

Bachelor's degree

Master's degree

Tools

Python (standard library)

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.

Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.

View Citi’s EEO Policy Statement and the Know Your Rights poster.