# Core AI Engineer

**Company**: London Stock Exchange Group (LSEG)
**Location**: GBR-London-10 Paternoster Square
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Finance

**Apply**: https://lseg.wd3.myworkdayjobs.com/en-US/Careers/job/GBR-London-10-Paternoster-Square/Core-AI-Engineer_R0121927?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_57082bb3-7fb

## Description

The AI & Automation team at LSEG is transforming how the company operates through Artificial Intelligence, Automation, and Intelligent Engineering.

As a Core AI Engineer, you will play a leading technical role in designing, developing, and deploying AI solutions across the organisation.

**Design & Build AI Solutions**

- Design, develop, and deploy enterprise-grade AI products and services.

- Build agentic AI workflows, retrieval-augmented generation (RAG) solutions, and intelligent automation capabilities.

- Deliver scalable integrations with LLMs, MCP services, vector databases, and enterprise platforms.

- Develop reusable AI frameworks, accelerators, and engineering standards.

**Engineering Excellence**

- Lead technical implementation across multiple AI initiatives.

- Define solution architecture and engineering patterns for AI products.

- Build robust APIs, orchestration services, and automation pipelines.

- Drive non-functional requirements including security, scalability, resilience, and observability.

**Product & Platform Development**

- Partner with Product Managers, Data Scientists, and Engineering teams to deliver AI solutions from concept through production.

- Support the development of AI platforms that enable wider engineering adoption.

- Establish monitoring, evaluation, and governance processes for AI systems.

- Improve reliability and performance of deployed AI capabilities.

**Innovation & Technical Leadership**

- Act as a technical leader across AI initiatives.

- Mentor engineers and contribute to the development of AI engineering capability.

- Evaluate emerging AI technologies and identify opportunities to accelerate delivery.

- Provide technical direction on architecture, tooling, and engineering practices.

**Business Impact**

- Translate business challenges into scalable AI solutions.

- Define measurable KPIs and success measures.

- Ensure solutions drive productivity improvements, operational resilience, and customer value.

- Partner with stakeholders across Engineering, Operations, and Product organisations.

**Requirements**

- Extensive software engineering experience delivering production-scale solutions.

- Experience building AI or machine learning systems in enterprise environments.

- Demonstrated experience moving AI solutions from proof-of-concept into production.

- Strong understanding of software architecture, engineering standards, and operational practices.

- Ability to influence technical direction across teams without formal management responsibility.

**Technical Skills**

- Python and modern software engineering practices.

- Experience with Large Language Models (LLMs) and Generative AI.

- Agent frameworks and orchestration technologies.

- Retrieval Augmented Generation (RAG) architectures.

- API development and microservice design.

- Cloud-native development (Azure preferred).

- CI/CD automation and MLOps practices.

- Vector databases and knowledge retrieval platforms.

- Data modelling and distributed systems design.

- AI observability, evaluation, and governance frameworks.

## Skills

### Required
- Python
- Large Language Models (LLMs)
- Generative AI
- Agent frameworks
- Orchestration technologies
- Retrieval Augmented Generation (RAG) architectures
- API development
- Microservice design
- Cloud-native development
- CI/CD automation
- MLOps practices
- Vector databases
- Knowledge retrieval platforms
- Data modelling
- Distributed systems design
- AI observability
- Evaluation and governance frameworks

### Nice to have
- Experience within Financial Services or highly regulated environments
- Knowledge of Service Reliability Engineering (SRE) principles
- Experience developing AI-powered operational tooling
- Experience building internal AI platforms or developer enablement capabilities
- Familiarity with Microsoft AI ecosystem, Copilot technologies, and Azure AI services

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