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?
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. You’ll partner with senior stakeholders & Product Managers to define outcomes, guide technical direction, and ensure delivery is focused on value not AI experimentation for its own sake.
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You do not need to code day-to-day, but you must be credible in technical decision-making and able to challenge designs constructively.
What you’ll be doing
- 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
- Lead engineering execution across multiple teams, ensuring clear ownership boundaries, interfaces, and ways of working
- Drive pragmatic technical direction across AI systems: LLM/ML deployment patterns, model lifecycle, monitoring, and iteration
- Ensure production readiness: security, reliability, observability, governance, and cost awareness (FinOps mindset)
- Unblock teams through active problem-solving, dependency management, and escalation when needed
- Balance trade-offs across speed, quality, risk, and cost and communicate them clearly
- Manage stakeholder expectations with calm, credible leadership; run steering conversations and provide transparent updates
- Build a culture of continuous improvement and innovation, with disciplined delivery under strict timelines
What success looks like
- AI solutions move from idea to production with demonstrable business value
- Delivery stays outcome-focused despite technical uncertainty and complexity
- AI capabilities are operationally viable: monitored, reliable, secure, and cost-controlled
- Teams understand ownership and interfaces; dependencies don’t derail execution
- Stakeholders trust progress, decisions, and trade-offs even under pressure
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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
- Experience delivering AI in regulated or high-stakes environments (e.g., financial services)
- Familiarity with AI governance, ethics, and emerging regulation (e.g., EU AI Act)
- Exposure to LLM platforms (e.g., Bedrock / Foundry) and multi-tenant cost controls
- Consulting/client-facing delivery leadership and workshop facilitation
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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