AI Enablement Lead

We are looking for an AI Enablement Lead who can make teams self-sufficient in building and deploying AI solutions— not someone who builds for them. This is a coaching and upskilling role with genuine technical depth. You need to know how production AI systems work so you can credibly train others to build, test, document, and govern their own. You will work across OBG's Technology function, driving adoption of our internal AI platform (KAIT) from early-adopter usage into mainstream, habitual practice. That means running workshops where people leave having built working solutions themselves, coaching product and engineering teams through scoping and delivering their own AI use cases, and ensuring that security, data governance, and release processes are embedded from the very first conversation — not bolted on at the end. Today, roughly 20% of employees generate 80% of AI platform activity. Your job is to close that gap within Tech — upskilling teams so they move from occasional use to confident, governed, daily AI-assisted working. Where you spot gaps in process, documentation, or governance, you flag them and work with the relevant owners to close them.

• Design and deliver hands-on technical workshops for Tech teams — the kind where participants build and ship working AI agents themselves, not watch someone else do it.
• Coach engineers and domain experts through identifying real use cases in their function, scoping them rigorously, and building their first working solutions on KAIT. Your success is measured by what they can do independently after you've worked with them.
• Run structured AI opportunity audits within Tech teams: helping teams assess which use cases are quick wins they can deliver themselves, which need Architect-level guidance, and which are not worth pursuing.
• Create technical training content covering topics such as RAG (connecting AI to internal knowledge bases), AI agent design, prompt engineering, and API integration — written for a Tech audience, grounded in real OBG scenarios.
• Provide floor support during workshop sessions, including live debugging and troubleshooting — guiding participants through solving problems, not solving for them.
• Ensure security, data governance, and compliance are embedded into every use case from the start — not treated as a gate at the end. Train teams to think about data classification, human oversight, and audit requirements as part of their design process.
• Upskill teams on OBG's Technology Release Process so they can self-serve: preparing documentation, completing governance checklists, and meeting production standards without needing hand-holding.
• Identify gaps in existing processes, documentation, or governance frameworks and flag them to the relevant owners. Where guidance is missing or unclear, work with A&I and platform teams to close those gaps through training and upskilling.
• Coach Tech Innovators (domain experts building use cases) through the full lifecycle: from identifying where AI adds genuine value, through scoping and prototyping, to handing off complex builds for Architect-level support where needed.
• For high-complexity use cases (multi-system integrations, MCP connectors, RAG pipelines), guide and upskill the teams responsible for delivery rather than owning the build yourself. Your role is to transfer capability, not accumulate it.
• Assess incoming use cases and route them correctly: straightforward agent builds that teams can own, strategic projects needing deeper technical support, and cases that belong in data science or other disciplines rather than KAIT.
• Deliver structured workshops and a best-practice guide for coding assistant adoption (e.g. GitHub Copilot, Cursor) across engineering teams. Engineering team leads retain accountability for sustained adoption in their teams.
• This is a secondary workstream. If the main AI enablement pipeline requires full capacity, developer productivity work is deprioritised.

• 4–7 years in a hands-on technical role — data engineering, AI/ML engineering, solutions architecture, or DevOps — with a subsequent move into enablement, consultancy, or internal transformation.
• Proven experience coaching technical teams to build and deploy AI agents or RAG pipelines in production — not just building them yourself.
• Hands-on with at least one low-code/no-code automation platform (e.g. n8n) — enough to credibly train others.
• Strong prompt engineering knowledge: system-level prompts, structured output, chain-of-thought, evaluation techniques — and the ability to teach these to others.
• Solid understanding of enterprise integration patterns: REST APIs, OAuth/SSO authentication, rate limiting, data flow between systems.
• Demonstrable commitment to governance and process: you embed security, data classification, and compliance into how teams work, and you flag gaps when processes are missing or unclear.
• Track record of delivering technical workshops where participants built tangible solutions themselves — not lecture-based training.
• Ability to translate complex technical concepts clearly for non-technical audiences and present credibly to senior stakeholders.
• Experience in a regulated industry: igaming, fintech, or financial services.
• Hands-on n8n experience for production workflow automation.
• Familiarity with MCP (Model Context Protocol) or similar frameworks for connecting AI agents to enterprise systems.
• Experience with LLM providers (OpenAI, Anthropic) for inference and evaluation.
• Working knowledge of vector databases, embedding models, and semantic search.
• Experience with coding assistants (GitHub Copilot, Cursor) in a developer productivity context.
• Multi-site or international delivery experience.

• Well-being allowance
• Learning and development opportunities
• Inclusion networks
• Charity days
• Long service awards
• Social events and activites
• Private medical insurance
• Life assurance and income protection
• Employee Assistance Programme
• Pension