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

Company Overview

Role Overview

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-facing applications.
• Establish guardrails, AI evaluation frameworks, observability standards, model monitoring, prompt testing, and responsible AI practices.
• Lead technical decision-making around AI infrastructure, deployment patterns, cloud architecture, model hosting, orchestration, and scalability.
• Support the development of AI governance frameworks covering data privacy, security, access controls, explainability, quality assurance, and risk management.

Capability Development & Adoption

• Develop an AI Engineering Maturity Model to assess and improve AI adoption across engineering teams.
• Deliver internal workshops, technical training sessions, and practical enablement programmes for developers, architects, and engineering leaders.
• Train teams on LLM engineering workflows, AI coding assistants, agentic architectures, RAG implementation, and AI observability practices.
• Create and maintain internal documentation, coding standards, architecture guidelines, and engineering playbooks.
• Stay current with emerging AI technologies and continuously identify opportunities to strengthen the organisation’s AI engineering capability.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related technical discipline.
• At least 7 years of software engineering experience, including strong exposure to software architecture, enterprise applications, APIs, cloud platforms, and modern software delivery practices.
• At least 3 years of practical experience designing and deploying production LLM applications, AI agents, RAG systems, or intelligent automation solutions.
• Demonstrated experience leading AI engineering initiatives across multiple engineering teams or business units.
• Strong hands-on knowledge of LLM application development, prompt engineering, RAG, agentic architectures, MCP, model evaluation, orchestration frameworks, and AI observability.
• Experience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack, or similar technologies.
• Experience working with vector databases and knowledge systems such as Pinecone, Weaviate, pgvector, Qdrant, Chroma, or similar tools.
• Strong programming ability in Python and ideally TypeScript, with experience developing APIs using FastAPI or similar frameworks.
• Strong understanding of REST APIs, Docker, Kubernetes, Git, CI/CD, cloud deployment, and enterprise software integration.
• Experience with AWS, Microsoft Azure, Google Cloud Platform, or multi-cloud environments.
• Familiarity with AI observability and evaluation tools such as LangSmith, Helicone, MLflow, Weights & Biases, OpenTelemetry, or similar platforms.
• Proven ability to mentor engineers, influence technical direction, establish standards, and communicate complex AI concepts to both technical and non-technical stakeholders.

Preferred Experience

• Building or leading AI Engineering Centres of Excellence.
• Establishing enterprise AI governance, responsible AI, and AI risk-management frameworks.
• Driving AI transformation programmes across software engineering organisations.
• Defining AI coding standards, AI-SDLC frameworks, and AI-assisted development methodologies.
• Building enterprise LLM platforms, internal AI tools, reusable AI components, or shared AI services.
• Implementing enterprise RAG, multi-agent architectures, MCP servers, and AI workflow automation.
• Measuring productivity, quality, adoption, and delivery improvements resulting from AI-enabled software development.
• Experience within financial services, fintech, trading, payments, regulated technology environments, or large-scale enterprise platforms.

Key Technology Areas

• Frontier LLMs: Claude, OpenAI GPT models, Gemini, Mistral, Llama, DeepSeek
• AI Development Tools: Claude Code, Cursor, GitHub Copilot, Gemini CLI, Windsurf, OpenAI Codex, Continue.dev
• Agent Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack
• Automation: n8n, Make, Zapier, Apache Airflow
• Knowledge Systems: Pinecone, Weaviate, pgvector, Qdrant, Chroma, LlamaIndex
• Engineering: Python, TypeScript, FastAPI, REST APIs, Docker, Kubernetes, Git, CI/CD
• Cloud Platforms: AWS, Azure, Google Cloud Platform
• AI Observability: LangSmith, Helicone, MLflow, Weights & Biases, OpenTelemetry