SENIOR MACHINE LEARNING ENGINEER (Lisbon office x1 day a week *OR* fully remote in Greece)
Xcede are delighted to be working with one of the world's largest gaming companies on the expansion of their existing Data Science & Machine Learning Division. The organisation has an enviable wealth of digital / B2C data available to them given the scale of their userbase. The company would like to capitalise further on this, hence their ambitious (but well-defined) hiring plans.
In particular, we're currently on the lookout for an experienced Data Science Team Lead – someone who combines the best of Machine Learning, Statistics, Model Deployment, and Managerial experience. The main aim of this unit is to facilitate and enable the ML unit's key projects. These can range from customer personalisation, recommendation engines, pricing algorithms, fraud algorithms, marketing models, and A/B tests.
As a Machine Learning Engineer, you will focus on delivering robust and scalable Machine Learning pipelines and you will support the deployment & running of Machine Learning models.
Responsibilities:
1. Design, develop, and maintain scalable model training and inference pipelines in a distributed production environment.
2. Build and manage Python-based microservices to host and serve machine learning models in production.
3. Develop internal tools to support CI/CD/CT, feature engineering, experiment tracking, and versioning of both data and models.
Requirements:
1. Hands-on experience with Python in a production environment.
2. Commercial experience demonstrating expertise in:
1. Software system design
2. The end-to-end machine learning project lifecycle
3. MLOps tools and practices (e.g., MLflow, Kubeflow)
4. Machine learning algorithms and models
4. Experience working with cloud platforms such as Microsoft Azure or AWS.
5. Familiarity with PySpark and Databricks.
If this role interests you and you would like to find out more, please apply here or contact us via niall.wharton@Xcede.com (feel free to include a CV for review).
#J-18808-Ljbffr