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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML/DataOps Manager - **Company:** FDJ UNITED - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Automation of Tests, Cloud Computing, Continuous Delivery, Data Architecture, Information Engineering, DevOps, Programming Tools, Distributed Computing Environment, Monitoring of Systems, Machine Learning, DataOps, Azure Machine Learning, Software Deployment, Data Streaming, Apache Spark, Cloudformation, Infrastructure Automation Frameworks, Apache Kafka, Machine Learning Operations, Data Lakehouse, Terraform, Data Pipelines - **Published:** August 4, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=38b86f7af8661bdd ## About the Role We think that to be successful in this role you will be able to demonstrate many of the following attributes: * 5+ years of relevant commercial experience, including experience in DataOps, MLOps, or platform engineering roles. * Proven experience leading or mentoring engineers in a technical environment. * Strong understanding of data engineering and machine learning workflows and how they operate in production. * Experience implementing CI/CD pipelines for data and/or machine learning systems. * Experience with Infrastructure as Code (Terraform, CloudFormation, or similar). * Strong experience with cloud platforms, ideally AWS. * Experience with distributed data processing technologies (e.g. Spark) and streaming platforms (e.g. Kafka). * Ability to operate at both a strategic and hands-on level when required. * Strong communication and stakeholder management skills, with the ability to align cross-functional teams. * Proactive mindset with the ability to navigate ambiguity and drive continuous improvement. Nice to Have * Experience in sports betting, trading platforms, or regulated industries. * Familiarity with modern ML tooling (e.g. feature stores, model registries, experiment tracking platforms). * Knowledge of data architecture patterns such as Data Mesh or medallion architecture. * Experience with observability and monitoring tools for data and ML systems. * Experience building internal platforms or developer tooling. * Passion for mentoring and building high-performing teams. ## Description FDJ United's ambition is to be the most insight-driven gambling company, and in recent years we've invested heavily in our data and machine learning capabilities. Focusing on our Sportsbook product, we're looking for a DataOps / MLOps Manager to lead the evolution of our ML platform and data operations capability, enabling faster, more reliable, and scalable delivery of data and machine learning products. This role will be central to building and operating the foundations that support end-to-end machine learning workflows - from research and experimentation through to production deployment and monitoring. You'll lead a team responsible for DataOps and MLOps practices, aligning closely with Data Engineering, Machine Learning Engineering, Quants, and Data Science teams to ensure seamless collaboration and delivery. As we scale our personalisation, customer risk, and trading optimisation capabilities, you'll play a key role in shaping the tooling, standards, and processes that enable teams to iterate quickly while maintaining high levels of reliability, governance, and compliance. What You'll Do * Lead and grow a team of DataOps and MLOps engineers, providing technical direction, coaching, and career development. * Define and drive the strategy for DataOps and MLOps capabilities across the sportsbook data platform. * Build and evolve the platform and tooling that supports end-to-end machine learning lifecycle management (development, deployment, monitoring, and retraining). * Establish best practices for data and machine learning workflows, including automated testing, deployment, and rollback strategies. * Work with DevOps to evolve infrastructure as code practices (e.g. Terraform, CloudFormation) to ensure scalable and reproducible environments. * Collaborate with Data Engineers, Quants, and ML Engineers to improve developer experience, platform usability, and delivery velocity. * Implement robust monitoring, observability, and alerting across data pipelines and ML models (including model performance and drift). * Drive standardisation of tooling and workflows across teams, balancing flexibility with consistency. * Ensure data and model governance practices are embedded, including reproducibility, lineage, discoverability, and compliance. * Partner with product and business stakeholders to align platform capabilities with strategic priorities. * Manage platform reliability, performance, and cost efficiency within AWS. What You'll Work On * A modern data and machine learning platform supporting both batch and real-time use cases. * Tooling and infrastructure for experiment tracking, model deployment, feature management, and monitoring. * A Data Lakehouse architecture supporting both analytical and ML workloads. * CI/CD pipelines and infrastructure that enable rapid, safe iteration for data and ML teams. * Cross-team enablement to improve how data and ML products are developed, deployed, and operated. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [#90DaysOfDevOps - The DevOps Learning Journey](https://www.wearedevelopers.com/videos/548-90daysofdevops-the-devops-learning-journey) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Implementing Feature Environments with AWS and Terraform](https://www.wearedevelopers.com/videos/531-implementing-feature-environments-with-aws-and-terraform) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Best Companies to work for in London: Top 25 Companies in 2023](https://www.wearedevelopers.com/magazine/187-best-companies-to-work-for-in-london-top-25-companies-in-2023) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)