AI Engineering Lead – LLM Engineering, AI Development & Intelligent Automation

Company Overview

Our client, a global and rapidly growing financial services group, operates across international markets through a technology-driven, regulated business model. The organisation is investing significantly in Artificial Intelligence, advanced analytics, intelligent automation, and next-generation software engineering capabilities to enhance its platforms, products, internal operations, and customer experience.

Role Overview

We are seeking an experienced AI Engineering Lead to establish and drive the organisation’s LLM Engineering capability and accelerate AI-powered software development across multiple technology teams. This role will define the standards, architecture, tooling, governance, and development practices required to build secure, scalable, production-ready AI solutions. You will guide engineering teams in adopting modern AI-assisted development workflows, leveraging frontier LLMs, agentic architectures, enterprise RAG systems, and intelligent automation platforms.

Working closely with Engineering Managers, Solution Architects, Product Owners, and senior leadership, you will ensure that AI becomes an embedded part of the software development lifecycle. The role combines hands-on technical leadership, architecture oversight, team enablement, and enterprise-wide AI transformation.

Responsibilities

AI Engineering Leadership & Delivery

• Lead the design, development, and implementation of enterprise-grade LLM applications, AI agents, intelligent automation solutions, and AI-native software products.
• Guide and mentor engineering teams on AI architecture, prompting strategy, agent design, model selection, code quality, and production deployment.
• Conduct AI solution design and technical architecture reviews before development begins.
• Establish engineering standards for secure, scalable, maintainable, and measurable AI implementations.
• Support teams in adopting AI-first development methodologies while maintaining strong software engineering quality, security, and delivery discipline.

AI-Assisted Software Engineering

• Champion the use of AI coding and development environments including Claude Code, OpenAI Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, and emerging AI engineering tools.
• Define enterprise standards for AI-assisted software engineering, developer prompting, code generation, review workflows, and responsible use of AI development tools.
• Develop reusable prompt libraries, engineering playbooks, technical documentation, and best-practice frameworks.
• Introduce practical AI-assisted development workflows that improve engineering productivity, code quality, testing, documentation, and delivery speed.
• Establish methods to measure and improve developer productivity through AI-enabled engineering practices.

LLM Engineering & Agentic Systems

• Design and optimise production-grade LLM applications using leading models including Claude, OpenAI, Gemini, Mistral, Llama, DeepSeek, and other suitable providers.
• Build AI agents and multi-agent workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack, and LlamaIndex.
• Design and implement enterprise RAG systems using vector databases, knowledge graphs, structured data sources, and internal documentation repositories.
• Develop scalable MCP servers, tool integrations, API connectors, and secure AI access layers for enterprise systems.
• Evaluate new LLMs, frameworks, AI platforms, and engineering tools, making recommendations for adoption based on technical fit, security, cost, performance, and business value.

AI Platform, Architecture & Governance

• Define enterprise AI architecture standards, reference architectures, reusable components, and internal AI platform capabilities.
• Design secure integrations between LLMs, internal systems, APIs, databases, workflows, and customer