Core AI Engineer

About the role

LSEG's AI & Automation team is building enterprise-scale AI capabilities that improve service resilience, automate workflows and accelerate engineering productivity. As a Core AI Engineer, you will design, develop and deploy AI solutions across the organisation, combining software engineering, machine learning and AI platform expertise. This is a hands-on engineering role for someone who wants to build scalable AI systems and influence enterprise AI adoption.

Key responsibilities

• Design, develop and deploy enterprise-grade AI products, including agentic AI workflows, RAG solutions and intelligent automation
• Build scalable integrations with LLMs, MCP services, vector databases and enterprise platforms
• Develop reusable AI frameworks, accelerators and engineering standards
• Define solution architecture, build APIs, orchestration services and automation pipelines, and drive security, scalability, resilience and observability
• Partner with Product Managers, Data Scientists and Engineering teams, and support AI platforms and monitoring, evaluation and governance of AI systems
• Act as a technical leader, mentor engineers and evaluate emerging AI technologies
• Translate business challenges into scalable AI solutions with measurable KPIs and partner with Engineering, Operations and Product stakeholders

Required profile

• Extensive software engineering experience delivering production-scale solutions
• Experience building AI or ML systems in enterprise environments and moving them from proof-of-concept to production
• Strong understanding of software architecture, engineering standards and operational practices
• Ability to influence technical direction across teams without formal management responsibility
• Desirable: financial services or regulated environment experience, SRE knowledge, internal AI platform experience, and familiarity with Microsoft AI ecosystem and Azure AI

Required skills

• Python and modern software engineering practices
• Large Language Models (LLMs) and Generative AI
• Agent frameworks, orchestration technologies and RAG architectures
• API development, microservices and cloud-native development (Azure preferred)
• CI/CD automation, MLOps, vector databases and data modelling
• AI observability, evaluation and governance frameworks