MLOps Engineer

MLOps Engineer | Python, Azure, Docker, CI/CD | Machine Learning in Production Anyone can train a model. Keeping it fast, reliable and live for millions of players is the hard part, and that's your job. We're hiring an MLOps Engineer for a global gaming technology company. You'll build the platforms, pipelines and serving systems that take machine learning from experiment to production, and keep it running. What you'll actually do - Serve models at scale. Design and run scalable inference and serving systems for ML workloads. - Automate everything. Build and maintain automated data, training and inference pipelines. - Own the release flow. Build and manage CI/CD pipelines for testing, validation and deployment. - Keep APIs healthy. Monitor deployed APIs for performance, reliability and security. - Build the platform. Create internal tools that teams use to configure and manage ML systems in production. - See problems first. Build observability dashboards and alerting for model and infrastructure health. - Test it properly. Write unit and integration tests for ML code, pipelines and deployment workflows. - Ship securely. Follow security best practice in containerised deployments and data handling. What you'll bring - Strong Python, with solid software architecture and development skills - Real experience deploying and managing ML models in production - Expertise in cloud platforms, ideally Microsoft Azure, for scalable and reliable ML systems - Docker and containerisation for deployments that scale and are easy to maintain - Monitoring, logging and alerting for ML systems and infrastructure - Version control (Git), package management and dependency tracking - Data modelling, ETL and databases, both SQL and NoSQL - A good grasp of core machine learning concepts and algorithms - A degree in Computer Science, Engineering or a related field (Bachelor's or Master's) Why you'll like it - Real scale: your systems power products used by millions of players worldwide. - Real ownership: you build the platform other engineers and data scientists rely on. - Real craft: a strong engineering culture with automation, testing and observability built in from the start. - Real impact: you're the reason models actually make it to production.