Generative & Agentic AI Strategy Lead

Location: London (Hybrid – London office with selective client travel)

Salary: £80,000 – £100,000 + bonus + benefits

Clearance: SC Cleared or eligible for SC Clearance

Overview

We are working with a fast-growing data and AI consulting business helping organisations understand and deploy modern AI capabilities in real operational environments.

As part of the next stage of growth, the firm is looking to hire a Generative & Agentic AI Strategy Lead who can help shape how organisations adopt and deploy large language models, generative AI systems, and emerging agentic architectures.

This is a client-facing consulting role combining AI strategy, technical credibility, and practical delivery leadership. The successful individual will work directly with senior stakeholders to move conversations from AI curiosity and experimentation into tangible, real-world applications.

The role requires someone comfortable operating at multiple levels — from educating executive audiences through to shaping solution architecture and delivery approaches with engineering teams.

The Role

The Generative & Agentic AI Strategy Lead will sit at the intersection of AI innovation, client advisory, and delivery enablement.

You will help organisations understand how modern AI systems actually work, where they create value, and how they can be implemented responsibly and effectively.

This includes shaping initiatives involving:

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Agentic AI systems and multi-agent architectures
• AI-assisted decision systems
• Generative AI applications embedded within enterprise workflows

You will work across the full lifecycle of AI initiatives — from early strategic conversations and concept development through to proof-of-concept, MVP delivery, and real operational deployment.

Key Responsibilities

AI Strategy & Advisory

• Advise organisations on the strategic adoption of generative AI and agentic systems
• Help clients understand the opportunities, limitations, and practical implications of modern AI architectures
• Translate emerging AI capabilities into real use cases aligned to business value

Client Engagement

• Operate as a trusted advisor to senior client stakeholders
• Lead conversations with both technical and non-technical audiences
• Simplify complex AI concepts and cut through industry hype to focus on practical outcomes

Solution Development

• Help shape AI-enabled products and solutions using modern architectures including:
• LLM-based applications
• RAG pipelines
• AI orchestration layers
• agent-based systems
• Work closely with data scientists, ML engineers, and data engineers to move ideas from concept to implementation.

Evangelism & Thought Leadership

• Act as an internal and external advocate for practical AI adoption
• Help educate clients and teams on the next generation of AI capabilities
• Contribute to the organisation’s AI thought leadership and innovation agenda

What We’re Looking For

This role requires someone who combines technical credibility with consulting polish.

The successful candidate will be comfortable:

• Speaking in rooms with deep technical specialists
• Presenting to executives who are still early in their AI journey
• Bridging the gap between concept, architecture, and real-world delivery

We are looking for someone who can operate as a mixture of:

• Evangelist – helping organisations understand what modern AI makes possible
• Educator – explaining complex AI concepts clearly and pragmatically
• Doer – turning ideas into real, deployable solutions

Experience

• Experience working in consulting, advisory, or client-facing technology roles
• Strong understanding of generative AI ecosystems and large language models
• Exposure to modern AI architectures and system design
• Experience shaping or delivering AI, machine learning, or advanced analytics initiatives
• Ability to move from strategic conversation to practical implementation
• Strong communication and stakeholder engagement skills

Technical Awareness

Candidates should have familiarity with areas such as:

• Large Language Models and generative AI ecosystems
• Model selection, prompting strategies, and orchestration
• Retrieval-Augmented Generation (RAG)
• Multi-agent or agentic AI frameworks
• AI safety, governance, and responsible AI
• Cloud AI platforms (Azure, AWS, or GCP)

Hands-on coding is not essential, but a strong conceptual understanding of how modern AI systems are designed and deployed is important.