Senior Machine Learning Engineer

Our client is a leading European GameTech company specialising in online sports betting and gaming, with operations across multiple countries. They’re currently seeking Machine Learning Engineers to join their AI department. In this role, you’ll focus on building robust and scalable machine learning pipelines and play a key part in the operationalisation of ML models, ensuring their effective integration into production environments.

Key Responsibilities

• Collaborate with Data Scientists: Work closely with data scientists to deliver production-ready model training pipelines.
• Design Scalable Solutions: Create scalable solutions within a distributed environment to support large-scale data processing.
• Design Inference Pipelines and Architectures: Build scalable inference pipelines and architectures to ensure real-time performance and efficiency.
• Implement Model Diagnostics: Develop model diagnostics including evaluation, monitoring, and alerting flows to track model performance.
• Automate Model Deployment: Enable automation of model deployment and integrate REST API model endpoints for seamless operations.
• Develop Scalable Model Serving Flows: Create efficient, high-performance model serving flows to meet production requirements.
• Optimise Clusters for Scalability: Work on cluster optimisation to ensure scalability and high availability of systems.
• Maintain Internal Tools and Libraries: Develop and maintain internal tools and libraries to streamline machine learning workflows.
• Write CI/CD/CT Pipelines: Design and implement Continuous Integration, Continuous Delivery, and Continuous Testing (CI/CD/CT) pipelines for automated deployment.

What You Bring

• Extensive Experience in Software Development: Demonstrated expertise in Python development within large-scale, high-performance production environments.
• Hands-on Experience in Designing ML Software Systems: Proven ability to design and build machine learning systems from the ground up, with a deep understanding of the ML project lifecycle and practical knowledge of ML algorithms and models.
• MLOps Concepts and Tools: Familiarity with MLOps concepts and tools like MLflow or Kubeflow for managing and automating machine learning workflows.
• Experience with Cloud Environments: Significant experience working with cloud platforms such as Microsoft Azure or AWS.
• Hands-on Experience with Spark and PySpark: Practical experience in using Spark and PySpark for distributed data processing.
• Strong Problem-Solving and Communication Skills: Excellent ability to analyse complex issues and communicate solutions effectively.

Why Apply Now?

With a strong focus on technology, data analytics, and user experience, our client is committed to innovation, responsible gaming, and continuous improvement. Recognised for its workplace culture and industry performance, our client has earned numerous awards and consistently ranks among the top employers in the sector.

Are you ready to take the next step in your career? Send your CV to ari.kilab@robertwalters.com