Senior Machine Learning Engineer in London

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: FDJ United aims to become the most insight-driven gambling company, with machine learning and generative AI at the heart of how we deliver customer experiences, optimise operations, and generate actionable insights. This role is primarily focused on building scalable, end-to-end machine learning solutions — from closely partnering with Data Scientists on model development to deploying high-performance services. You’ll act as a technical link between our machine learning and AI initiatives, helping integrate models into core ML pipelines where they drive measurable value. You’ll work on strategic use cases such as customer engagement, automated insight generation, and decision-support tooling. You’ll also help shape the shared tooling frameworks, and infrastructure that support experimentation, model iteration, dashboard monitoring and safe, responsible AI deployment. If you love problem solving, machine learning, and transforming ideas into working and production-level solutions this is the role for you. The role also gives ample space to direct Data Science contribution, helping ideate and propose novel solutions to different and exciting problems.

What you will do:

• Machine Learning Engineering: Build, deploy, and maintain ML models as services, streams, or batch jobs across our real-time and offline platforms. Develop scalable model APIs with strong CI/CD and observability practices. Implement model testing, monitoring and rollback capabilities in production environments. Partner with Data Scientists to translate prototypes into reliable, maintainable ML applications. Directly identify and develop ML solutions in collaboration with Data Scientists.
• Platform & Tooling: Drive automation and reusability of ML infrastructure using containers (Docker), Kubernetes, and Terraform. Support and develop dashboard monitoring of key ML/AI models and services. Ensure cloud-native solutions in AWS with secure, resilient and cost-effective deployment strategies. Contribute to the development of MaaS platform and shared tooling for model deployment and experimentation.
• Compliance: Ensure that you adhere to the Governance, Risk & Compliance (GRC) obligations for your role. Identify and raise any non-compliance incidents promptly to your line manager. Challenge processes, policies and projects that will negatively impact compliance within the Group. Complete all mandatory compliance training assigned to you. Reach out to the Compliance Teams if unsure of any of your compliance obligations or the requirements are unclear.

About You: You’ll bring deep experience in machine learning engineering and a keen interest in modern AI technologies. You thrive in collaborative teams and are motivated by building solutions that scale. You are proactive and enjoy both the creation as well as the delivery processes. 3–5 years’ experience in machine learning engineering and data science. PhD or Masters in a numerate discipline. Excellent programming skills in Java and Python for production systems. Strong ML/Data Science fundamentals. Experience deploying ML models as APIs, batch jobs and streaming services (e.g., Kafka Streams). Practical experience deploying containerised apps with Kubernetes. Past experience in developing ML solutions from conception to delivery. Excellent interpersonal and collaboration skills. Up-to-date knowledge of the latest Data Science and ML developments.