AI Enablement Lead

Join Kindred Group as an AI Enablement Lead to coach teams in building and deploying AI solutions. Drive the adoption of AI practices across the organization, ensuring governance and security are embedded from the start. At FDJ UNITED, we don't just follow the game, we reinvent it. FDJ UNITED is one of Europe’s leading betting and gaming operators, with a vast portfolio of iconic brands and a reputation for technological excellence. With more than 5,000 employees and a presence in around fifteen regulated markets, the Group offers a diversified, responsible range of games, both under exclusive rights and open to competition. We set new standards, proving that entertainment and safety can go hand in hand. Here, you’ll work alongside a team of passionate individuals dedicated to delivering the best and safest entertaining experiences for our customers every day. We’re looking for bold people who are eager to succeed and ready to level-up the game. If you thrive on innovation, embrace challenges, and want to make a real impact at all levels, FDJ UNITED is your playing field. Join us in shaping the future of gaming. Are you ready to LEVEL-UP THE GAME?

Technical Coaching & Upskilling

• •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.

Governance, Security & Process

• •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.

Use Case Pipeline & Enablement at Scale

• •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.

Developer Productivity (Secondary — ~20% of Time)

• •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.

• •The strongest candidates will have spent several years building production systems, then moved into a role where they had to make others successful at doing the same. You are not a career trainer who picked up AI recently. You are not a pure engineer who has never coached a team. You are the person who understands how a multi-step automated workflow works end-to-end, and whose instinct is to teach a team to build it rather than build it for them.
• •You have strong opinions about governance and process — not because you enjoy bureaucracy, but because you’ve seen what happens when teams deploy without proper data classification, documentation, or human oversight. You embed these from the start, and you flag it when the processes themselves need fixing.
• •You are comfortable with ambiguity, confident enough to challenge senior stakeholders when a use case does not stack up, and pragmatic enough to help a team ship something useful rather than wait for something perfect.

• •Direct impact: you are the single point of contact for AI capability uplift across a Tech function of 1,000+ people. The organisation’s AI strategy targets depend on teams being able to build and govern AI solutions independently — and you are the person who makes that happen.
• •Breadth: in a single week you might coach an engineering manager through scoping their first AI use case, review a solution design for governance readiness, run a workshop for a product team, and flag a gap in release process documentation.
• •Shape the standard: you will define what good looks like for AI adoption in Tech — not just training content, but the processes, governance habits, and quality standards that teams follow when they build.
• •Regulated, high-stakes environment: OBG operates in igaming under strict regulatory oversight. Security and governance are not optional extras here — they are the baseline.

Essential

• •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.

Highly Desirable

• •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.