Product Manager, AI

LSEG•New York, NY

23h•Hybrid

About The Position

LSEG (London Stock Exchange Group) is more than a diversified global financial markets infrastructure and data business. We are dedicated, open-access partners with a commitment to excellence in delivering the services our customers expect from us. With extensive experience, deep knowledge and worldwide presence across financial markets, we enable businesses and economies around the world to fund innovation, manage risk and create jobs. It’s how we’ve contributed to supporting the financial stability and growth of communities and economies globally for more than 300 years. We are looking for a hands-on AI Product Co-Developer to join our team building production-grade agentic AI systems for complex, data-intensive environments. This is a hybrid role sitting at the intersection of product thinking and engineering depth — you will both define what gets built and actively build it, working across the full lifecycle from discovery and requirements through to deployment and iteration.

Requirements

5+ years of experience spanning software or ML engineering and product development, or a closely related combination — we value technical depth and product ownership in equal measure
Demonstrated hands-on experience building with LLMs and/or agentic frameworks — shipped products or features preferred over academic work
Working knowledge of how large language models and agentic systems behave in production - including tool use, prompt design, orchestration patterns, output variability, and failure modes
Ability to write clear product requirements and define, review, and challenge technical specifications without requiring engineering support
Experience evaluating and testing AI outputs — defining acceptance criteria, identifying edge cases, and working with engineering teams to resolve model or integration issues
Solid Python skills and familiarity with APIs, data pipelines, and cloud infrastructure
Experience with real-time or near-real-time data systems, with a natural sensitivity to latency, throughput, and cost trade-offs
Familiarity with responsible AI principles — including data quality, model performance monitoring, and bias considerations — and their implications for product design in regulated environments
Comfortable working across technical and commercial stakeholders — able to translate product decisions clearly for engineering teams and client-facing audiences alike
BA, BS, or Master's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience

Nice To Haves

Exposure to AI partner platforms or ecosystems in a product, technical, or commercial capacity is an advantage

Responsibilities

Contribute to the full AI product lifecycle: discovery, requirements definition, development, testing, and deployment
Design, build, and iterate on LLM-powered agentic workflows for complex, data-intensive use cases, applying sound orchestration patterns and tool-use design
Translate business and user needs into clear, actionable product requirements and agent configurations
Define and monitor product performance metrics and acceptance criteria for AI outputs in production — covering accuracy, latency, cost, and auditability
Manage the post-launch product lifecycle: track performance, gather user feedback, and contribute to model or feature refresh cycles
Contribute to system optimisation across performance, cost, and operational constraints
Collaborate with governance teams to ensure AI outputs meet internal quality, compliance, and interoperability standards
Maintain a forward-looking view on the evolving AI landscape — including model capabilities, agentic frameworks, and emerging protocol standards — and translate relevant developments into product opportunities
Engage with internal stakeholders and cross-functional teams to support successful delivery of AI capabilities
Support demos and presentations of prototypes and new capabilities to internal and external audiences
Build and share expertise in AI product design and agentic workflows across engineering, product, and domain teams