FDJ UNITED
AI Solutions Technical Engineering Manager
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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?
The Role
We’re looking for an AI Solutions Technical Engineering Manager to lead the delivery of AI- and data-enabled products and platforms from early shaping through to measurable outcomes in production. You’ll operate at the intersection of engineering leadership, AI delivery, and stakeholder alignment, turning ambiguous ideas into structured plans, unblocking teams, and ensuring solutions are operationally viable.
You will work across domain teams in a federated model, bringing clarity around ownership, interfaces, and accountability. This is a senior role for someone who can drive innovation while delivering under tight timelines and high expectations.
Responsibilities
- Lead multidisciplinary engineering teams (AI/ML, data, software, platform) to deliver AI capabilities that are trusted, adopted, and production-grade.
- Balance team management and technical work: reviewing designs and code, prototyping, and making decisions on deployment, routing, evaluation, and monitoring for our LLM and ML systems.
- Own technical direction of our text-to-SQL platform and serve as the technical backstop for agent and MCP work.
- Decide on model approaches: whether a problem needs fine-tuning or better prompting, retrieval, or routing, backed by evaluation data.
- Ensure production-grade quality: secure, observable, cost-sensible, reliable, and well-governed as they scale.
- Manage people effectively: coaching, growth, honest feedback.
- Represent AI delivery to senior stakeholders: staying calm and credible under tight timelines and open questions.
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What You’ll Be Doing
- Someone who has actually built and shipped AI/ML systems themselves, recently enough that they can still read and write code and hold their own in a design review.
- Real depth on LLM systems — RAG, agentic architectures, tool use, evaluation, guardrails, model lifecycle.
- Hands-on experience with multi-model or multi-provider setups — routing (semantic routers, OpenRouter or equivalents), fallback strategies, and cost/latency trade-offs across providers.
- Practical experience with MCP-based integrations and agent orchestration, including the judgment to know when an agentic approach is overkill.
- A working understanding of fine-tuning versus prompt/retrieval-based approaches, and the evaluation rigor to justify the choice with data.
- Experience leading a small senior team where you were still the most technical person in the room, or close to it.
- Comfortable making architecture decisions without a clear playbook, and confident enough to defend them on technical merit.
- Solid production engineering fundamentals - AWS, Kubernetes, CI/CD, observability.
- Genuinely plugged into what's happening in AI right now, and able to tell the difference between a real capability shift and a marketing claim.
- Communicates well enough to make technical trade-offs land with non-technical senior stakeholders, without dumbing anything down.
- Own delivery of AI workloads and AI-enabled products end-to-end: from discovery through build, launch, and iteration.
- Convert loosely defined ideas into delivery plans, milestones, and measurable outcomes.
What Success Looks Like
- AI ideas actually make it to production — not stuck in prototype limbo — and you can point to real business impact, not just "it's live".
- The team ships fast without cutting corners on evaluation, security, or cost — because you're close enough to the work to catch problems before they become production incidents.
- Your engineers see you as a technical peer they'd escalate a hard problem to, not just someone tracking their tickets.
- Text-2-SQL, the agent/MCP work, and model integrations stay reliable and well-governed as they scale — not held together by one person's tribal knowledge.
- When trade-offs get hard — speed vs. quality, build vs. buy, fine-tune vs. prompt — you make the call, back it with data, and stakeholders trust it even when the timeline is brutal.
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Experience and Capabilities (Essential)
- Proven experience leading engineering delivery for data-heavy, AI-enabled, or platform products.
- Strong understanding of modern AI concepts: ML systems, LLMs, data dependencies, evaluation, and operational risks.
- Track record managing complex deliveries across multiple teams and stakeholders.
- Comfortable operating in federated/domain-oriented environments with shared ownership.
- Excellent communication: able to align senior stakeholders and guide teams through ambiguity.
- Solid grasp of production engineering fundamentals: cloud, reliability, security, monitoring, CI/CD.
Technical Environment
(Not hands-on coding daily, but technically credible)
- Cloud: AWS / Azure / GCP
- AI/ML delivery: model deployment, MLOps/LLMOps, monitoring, iteration
- Platform foundations: Kubernetes (EKS/AKS), CI/CD, GitOps concepts
- Observability: metrics, logs, tracing; dashboards and alerting disciplines
- Architecture: APIs, microservices, event-driven systems; data pipelines
Desirable
- Delivery experience in regulated or high-stakes environments (financial services, gambling/gaming compliance).
- Working knowledge of AI governance and where regulation like the EU AI Act is heading.
- Direct experience with Bedrock or similar enterprise LLM platforms, including cost and access controls across multiple teams and tenants.
- Experience with access-control-sensitive text-to-SQL or similar natural-language-to-structured-query systems.
- Some visible engagement with the AI community — talks, writing, open source — as evidence you're tracking the field, not just reading about it secondhand.
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