AI Product Co-Developer for Production Agentic Systems

London Stock Exchange Group; Paris; Hybride; EUR 120 000 - 180 000; Plein temps

Résumé du poste

London Stock Exchange Group is seeking a hands-on AI Product Co-Developer to help build production-grade agentic AI systems across the full lifecycle from discovery to deployment.

You will design, build, and iterate on LLM-powered workflows, translate business needs into concrete requirements, monitor performance metrics, and collaborate with governance, engineering, and commercial teams to deliver scalable AI capabilities in a hybrid work setting.

Qualifications

• 7+ years of software or ML engineering and product development.
• Hands-on experience building with LLMs or agentic frameworks.
• Knowledge of production LLM behavior, tool use, orchestration patterns.
• Able to translate business needs into clear product requirements.
• Strong Python skills with data pipelines and cloud infrastructure.

Responsabilités

• Contribute to the full AI product lifecycle from discovery to deployment.
• Design, build, and iterate on LLM-powered agentic workflows for complex use cases.
• Translate business needs into actionable product requirements and configurations.
• Define and monitor product performance metrics for AI outputs in production.
• Manage post-launch lifecycle: track performance and plan refresh cycles.
• Collaborate with governance for quality, compliance, and interoperability.
• Engage stakeholders to support delivery of AI capabilities.
• Support demos and presentations of prototypes and new capabilities.

Connaissances

Python

LLMs

Product ownership

APIs

Data pipelines

Cloud infra

Formation

BA/BS or MSc in CS/Engineering/Math

Outils

Cloud platforms

LLM platforms

Description du poste

London Stock Exchange Group is seeking a hands-on AI Product Co-Developer to help build production-grade agentic AI systems across the full lifecycle from discovery to deployment.

You will design, build, and iterate on LLM-powered workflows, translate business needs into concrete requirements, monitor performance metrics, and collaborate with governance, engineering, and commercial teams to deliver scalable AI capabilities in a hybrid work setting.