AI Strategy Lead

LSEG•New York, NY

2d•$147,500 - $245,900

About The Position

The AI Strategy Lead is a hands-on leadership role responsible for defining and executing the AI strategy across FX trading platforms and engineering functions. This role combines strategic ownership with direct technical involvement in building and scaling AI-driven solutions. Reporting to the Director of FX Exchange Engineering, this individual will work closely with engineering leads, product, and architecture teams to translate AI opportunities into production-grade solutions, with a strong focus on standardization, platform reuse, and measurable impact.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
10+ years of software engineering experience, with hands-on development background.
Demonstrated experience delivering AI/ML or data-driven solutions in production.
Strong programming experience (preferably Java or similar enterprise stack).
Solid understanding of: Distributed systems and APIs, Event-driven / messaging architectures, Integration of AI into production systems.

Nice To Haves

Experience in financial markets / trading systems (FX ideally).
Exposure to GenAI / LLM integration in enterprise environments.
Experience with MLOps or AI platform engineering.
Familiarity with developer tooling automation and test automation frameworks.

Responsibilities

Define and drive the AI strategy aligned to FX platform and engineering priorities.
Personally contribute to the design and implementation of AI/ML solutions (e.g., prototypes, frameworks, integrations).
Lead by example in moving from PoCs to production-grade systems.
Identify and prioritize high-impact use cases such as: AI-driven test automation, Intelligent workflow automation, Developer productivity (code generation, CI/CD optimization).
Work directly with teams on architecture, design, and implementation of AI solutions.
Contribute to code, frameworks, or reusable components where needed.
Establish engineering best practices for: Model integration into Java-based services, API-driven AI services, Messaging-based integration (e.g., event-driven systems).
Drive adoption of common libraries, shared frameworks, and reusable services.
Define and implement a shared AI platform approach aligned with enterprise architecture: Common APIs and services for AI capabilities, Reusable components across FX venues, Integration with existing platform constructs (e.g., messaging bus, FIX, shared services).
Avoid one-off solutions by enforcing platform-first design and reuse.
Partner with global engineering teams (NY, London, Hyderabad, Bangkok) to deliver AI-enabled capabilities.
Ensure clear ownership, execution tracking, and delivery accountability.
Actively unblock teams and resolve technical or execution challenges.
Drive best practices for deploying and managing AI solutions in production: Model lifecycle management, Monitoring, observability, and performance tuning, CI/CD integration for AI components.
Ensure solutions are scalable, secure, and compliant with enterprise requirements.
Implement practical governance for AI usage (data quality, explainability, auditability).
Ensure compliance with regulatory expectations for financial systems.
Define guardrails for GenAI adoption (security, data leakage, model usage).
Evaluate emerging technologies (GenAI, LLMs, AI agents) with a focus on real engineering impact.
Lead targeted PoCs—but with a clear path to production or discard.
Introduce tooling and frameworks that improve engineering productivity.
Mentor engineers and leads on AI/ML concepts and implementation patterns.
Upskill existing teams rather than relying solely on specialist roles.
Influence hiring strategy for targeted AI/ML skills where needed.