Machine Learning Engineer

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 creating maintainable, efficient, and pragmatic solutions, emphasizing automation, software engineering excellence, and MLOps practices. You will report directly to the Data Science Team Leader and collaborate closely with the US AgentOps Team Lead, responsible for agentic and model orchestration platforms, as well as our UK technical excellence center. Acting as a bridge between model development and platform engineering, your work will ensure seamless deployment, monitoring, and scaling of machine learning models, enabling the company to deliver innovative products rapidly and reliably.

Qualifications

• 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, proactive approach to system design, prioritizing speed, reliability, and business value over complex, theoretical infrastructure.
• Strong proficiency in Python programming, with a solid understanding of software engineering patterns, API development, and automated testing frameworks.
• Extensive hands-on experience with Google Cloud Platform (GCP), including Vertex AI services such as Pipelines, Endpoints, and Workbench.
• Practical experience with containerization tools like Docker and orchestration platforms such as Google Kubernetes Engine (GKE).
• Excellent communication skills, capable of translating complex technical concepts for data scientists and operational requirements for product teams.
• Experience utilizing Infrastructure as Code (IaC) tools such as Terraform to automate infrastructure provisioning.
• Familiarity with real-time streaming tools like Apache Kafka or GCP Pub/Sub for data ingestion and processing.

Responsibilities

• Owning the deployment of machine learning models to production, ensuring scalable and low-latency prediction endpoints using GCP Vertex AI.
• Designing, implementing, and maintaining CI/CD/CT pipelines for machine learning workflows leveraging Vertex AI Pipelines, Cloud Build, and other GCP tools.
• Establishing automated monitoring and alerting frameworks, such as Vertex AI Model Monitoring, to track data drift, model performance, and system health in real-time.
• Championing best practices for software engineering within the data science team, including robust unit testing, containerization, version control, and automation of 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.
• Ensuring the reliability, maintainability, and scalability of machine learning systems across production environments.

Benefits

Bet365 offers a comprehensive benefits package designed to support our employees' professional and personal growth. This includes competitive salary packages, health insurance, and wellness programs. Employees have access to ongoing training and development opportunities, fostering continuous learning. The company promotes a flexible working environment, encouraging work-life balance. Additionally, Bet365 provides various employee engagement initiatives, performance bonuses, and a collaborative workplace culture that values innovation and diversity.

Equal Opportunity

Bet365 is an equal opportunity employer. We are committed to fostering an inclusive environment where all employees and applicants are treated with respect and fairness. We do not discriminate based on race, ethnicity, gender, age, sexual orientation, disability, religion, or any other protected characteristic. Our hiring practices are designed to promote diversity and ensure equal access to opportunities for all qualified candidates.