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.
This job description provides a high-level summary of the types of work performed. Other job-related duties may be assigned as required.
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Job Family Group: Institutional Trading
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Job Family: Structuring
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Time Type: Full time
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Most Relevant Skills Please see the requirements listed above.
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Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.
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