Machine Learning Engineer

About The Company Bet365 is a globally recognized leader in the online gambling and sports betting industry, renowned for its innovative approach, extensive product offerings, and commitment to responsible gaming. With a strong presence in multiple markets worldwide, Bet365 continuously invests in cutting-edge technology to deliver a seamless and engaging experience to millions of users. The company values integrity, excellence, and customer satisfaction, fostering a dynamic environment where innovation and collaboration thrive. As part of its ongoing growth and digital transformation, Bet365 is dedicated to leveraging data science and machine learning to enhance its services and operational efficiency. About The Role We are seeking a highly pragmatic, results-driven Machine Learning (ML) Engineer to join our newly established US Data team. In this role, you will be instrumental in building reliable, automated infrastructure that powers our machine learning lifecycle. Your primary responsibility will be to operationalize and scale models developed by our data science team, transforming prototypes into robust, production-grade systems with high velocity. You will focus on developing maintainable, automated systems that emphasize practicality over over-engineering, ensuring that solutions are efficient, scalable, and aligned with business needs. This position reports to the Data Science Team Leader and involves close collaboration with the US AgentOps Team Lead, responsible for agentic and model orchestration platforms, as well as our UK-based technical excellence center. Your role acts as a bridge between model development and platform engineering, ensuring seamless deployment, monitoring, and scaling of machine learning models in production environments. The ideal candidate will possess a strong background in software engineering, automation, and MLOps practices, with a keen eye for delivering high-quality, reliable systems that support the company's strategic objectives. Qualifications The successful candidate will have proven experience as an ML Engineer, Data Engineer, or Software Engineer with a focus on deploying, monitoring, and scaling machine learning systems in production environments. A pragmatic and proactive approach to system design is essential, prioritizing speed, reliability, and business value over overly complex infrastructure. Strong proficiency in Python programming, coupled with a solid understanding of software engineering patterns, API development, and automated testing frameworks, is required. Extensive hands-on experience with Google Cloud Platform (GCP), particularly Vertex AI (including Vertex AI Pipelines, Endpoints, and Workbench), is highly desirable. Familiarity with containerization tools like Docker, container orchestration with Google Kubernetes Engine (GKE), and Infrastructure as Code (IaC) tools such as Terraform will be critical for success. Additionally, experience with real-time streaming technologies like Apache Kafka or GCP Pub/Sub, and setting up automated monitoring and alerting frameworks, will be advantageous. Excellent communication skills are necessary to translate complex technical concepts to diverse stakeholders, including data scientists and operational teams. Responsibilities Owning the deployment of machine learning models to production, building, and maintaining scalable, low-latency prediction endpoints using GCP Vertex AI.Designing, implementing, and maintaining CI/CD/CT pipelines for machine learning workflows utilizing Vertex AI Pipelines, Cloud Build, and other GCP tools to ensure efficient and reliable deployment cycles.Setting up automated monitoring and alerting systems, such as Vertex AI Model Monitoring, to track data drift, model performance, and system health in real-time, ensuring high availability and reliability.Championing best practices for software engineering within the Data Science team, including writing robust unit tests, implementing containerization, version control, and automating deployment processes.Collaborating closely with the Data Science Team Leader, Junior Data Scientists, and the AgentOps Team Lead to accelerate deployment cycles, eliminate operational bottlenecks, and maintain high deployment velocity.Participating in the development of scalable, maintainable infrastructure that supports rapid experimentation and deployment of machine learning models.Ensuring compliance with security, privacy, and data governance standards throughout the deployment pipeline. Benefits Bet365 offers a competitive benefits package designed to support the well-being and professional growth of its employees. This includes comprehensive health insurance, retirement plans, and generous paid time off. The company promotes a collaborative and inclusive work environment, providing opportunities for continuous learning, career development, and participation in innovative projects. Employees have access to state-of-the-art tools and technologies, fostering a culture of excellence and innovation. Additionally, Bet365 encourages work-life balance through flexible working arrangements and wellness initiatives, ensuring employees can thrive both professionally and personally. Equal Opportunity Bet365 is an equal opportunity employer committed to fostering an inclusive environment for all employees. We value diversity and are dedicated to providing equal employment opportunities regardless of race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, or any other protected characteristic. We believe that a diverse workforce enhances our ability to innovate and serve our global customer base effectively. All qualified applicants will receive consideration for employment without regard to any protected status.