Group Transformation – AI Technical Implementation Lead

Group Transformation – AI Technical Implementation Lead

Location: London, minimum four days per week onsite across Herne Hill and Carter Lane, with travel to other Group offices as required
Salary: Competitive, dependent on experience, plus company benefits
Contract: 18-month fixed-term transformation contract, with potential to extend

At Mark Allen, we create specialist content, events, data and insight for professional communities across sectors that matter.

As AI Technical Implementation Lead, you will be the hands-on technical force within our four-person AI Implementation Pod, a multidisciplinary, agile team at the centre of Mark Allen Group, supporting our wider Group transformation programme.

You will have the scope to prototype, build, test and iterate, working directly with colleagues across the business to understand real problems and determine where AI, automation or better systems integration can make a difference. From agents and automations to integrations and AI-enabled workflows, you will turn promising ideas into secure, scalable solutions that people can actually use.

The role has a dual reporting line to the Group COO for transformation priorities and outcomes and to the Group Development Director for technical direction, engineering standards and integration with the wider Development team. You will be an integrated member of that team, ensuring the Pod's work draws on existing capability and aligns with the Group's architecture and technology strategy.

Working alongside the AI Strategic Implementation Lead and supported by a Programme & Adoption Manager and Transformation Fellow, you will translate business opportunities into practical, reliable solutions. This is not a role for someone who wants to advise from the sidelines. We need someone who enjoys building but who is equally comfortable listening, asking questions, explaining technical decisions clearly and working with others to find the right solution.

You will also help shape how we build and implement AI at Mark Allen, establishing reusable technical patterns, evaluating emerging platforms and helping us make intelligent decisions about what to build, what to buy and what isn't worth pursuing.

What you’ll be doing

Turn business opportunities into working solutions

• Assess the technical feasibility of priority business opportunities, identifying where AI, automation, systems integration or simpler technical solutions will deliver the greatest value.

• Rapidly prototype, build, test and iterate AI-enabled solutions.

• Design and implement agents, automations, integrations and AI-enabled workflows.

• Take the strongest prototypes towards reliable production implementation.

• Develop reusable components and technical patterns that accelerate future implementation.

Evaluate what is genuinely worth using

• Evaluate emerging AI models, tools and platforms.

• Run structured pilots and proof-of-concept testing before wider rollout.

• Help make enterprise tooling decisions based on capability, interoperability, security, cost and user need.

• Help design and manage safe sandbox environments.

• Identify where we can extend existing platforms rather than introduce unnecessary software.

• Help us avoid uncontrolled AI and SaaS proliferation.

Connect technology with real business problems

• Work closely with the Group Development Director, Development and Data teams and colleagues across the business, ensuring technical delivery is integrated with existing capabilities and standards.

• Understand business requirements and translate them into practical technical implementation plans.

• Identify technical and data dependencies early.

• Ensure solutions align with our wider architecture and technology strategy.

• Create a practical bridge between emerging AI opportunities and our existing Development and Data capability.

• Explain technical options, limitations and trade-offs clearly to both technical and non-technical colleagues.

Understand performance, scalability and cost

• Monitor technical usage and performance.

• Assess token consumption, latency, reliability and cost implications.

• Evaluate value for money across different models and platforms.

• Help ensure decisions about scaling AI solutions are based on evidence rather than hype.

Build securely and responsibly

• Ensure solutions have appropriate security, permissions, privacy and data controls from the outset.

• Work within agreed Group governance and technical standards.

• Bring practical technical insight into the development of AI policy and governance.

Stay ahead of what’s changing

• Maintain strong awareness of emerging models, APIs, agents, developer tooling and enterprise AI infrastructure.

• Identify developments with genuine practical relevance to Mark Allen.

• Help us distinguish meaningful technical advances from noise and hype.

What you will be here to achieve

Over the 18 months, you will:

• Establish a robust technical framework for experimentation and implementation across AI, automation and systems integration.

• Create safe and scalable sandbox and pilot environments.

• Deliver working solutions for priority workflows.

• Build reusable technical components that reduce future implementation time and cost.

• Strengthen integration between the Pod's technical solutions and our existing Development and Data capabilities, ensuring alignment with Group architecture and engineering standards.

• Reduce reliance on unmanaged or fragmented tooling.

• Improve visibility of usage, technical dependencies and cost.

• Help high-value business opportunities move rapidly from idea to practical implementation, using AI, automation or systems integration where appropriate.

Success means valuable opportunities can move quickly from idea to working implementation while remaining secure, scalable, cost-conscious and aligned with our wider technology architecture.

Must-haves:

This is a hands-on technical role. You need to be comfortable building, not simply advising.

You will bring strong practical capability across areas including:

• Coding and development: Python and/or JavaScript/TypeScript, Git/version control and experience taking prototypes towards production.

• Applied AI: LLM APIs and SDKs, prompt orchestration, structured outputs, agentic workflows and tool/function calling.

• Integration and data: REST APIs, webhooks, system integrations, SQL and working with structured and unstructured data.

• AI architecture: retrieval, RAG, search and vector approaches where appropriate, alongside cloud environments and deployment.

• Performance and optimisation: evaluating model performance, reliability, latency, token usage and cost.

• Security: authentication, permissions, secure API use and responsible enterprise AI implement