About this role
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?
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.
The role
You will lead multidisciplinary engineering teams (AI/ML, data, software, platform) to deliver AI capabilities that are trusted, adopted, and production-grade.
Day to day, you're splitting time between running the team and doing real technical work alongside them — reviewing designs and code, prototyping when it's faster than explaining, and making the calls on deployment, routing, evaluation, and monitoring for our LLM and ML systems. That includes when to use semantic routing versus static routing, when OpenRouter-style multi-provider setups make sense, and how Claude and other models stay integrated cleanly across different tech stacks rather than bolted on inconsistently.
You'll own technical direction of our text-to-SQL platform. You're also the technical backstop for agent and MCP work: tool-use design, orchestration patterns, and knowing when MCP is the right call versus overkill.
On models, you decide whether a problem needs fine-tuning or whether better prompting, retrieval, or routing gets there faster and cheaper; backed by real evaluation data, not intuition.
Beyond that, you keep what we ship production-grade — secure, observable, cost-sensible, reliable. You manage the people side properly too: coaching, growth, honest feedback. And you represent AI delivery to senior stakeholders, staying calm and credible under tight timelines and open questions.
What you’ll be doing
Someone who has actually built and shipped AI/ML systems themselves, recently nough 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 — not just familiarity with the terms
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.
You're also the technical backstop for our KAIT platform - our LibreChat-based internal AI tool, covering MCP gateway architecture, agent orchestration, and integrations across Confluence, Jira, Teams, and ClickHouse.
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 - enough to have an opinion, not necessarily to build it yourself day to day
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
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
We believe talent knows no boundaries. Our hiring process focuses solely on your skills, experience, and potential to contribute to our team. We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics as the age, gender, origin, religion, sexual orientation, neurodiversity or disability.
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