We are Kaizen Gaming

Kaizen Gaming is the leading GameTech company in Greece and one of the fastest-growing in the world, operating in 13 markets with 2 brands, Betano & Stoiximan. 

We always aim to leverage cutting-edge technology, providing the best experience to our millions of customers who trust us for their entertainment.  

We are a diverse team of more than 2.200 Kaizeners, from 40+ nationalities spreading across 3 continents. Our #oneteam is proud to be among the Best Workplaces in Europe and certified Great Place to Work across our offices. Here, there’ll be no average day for you. Ready to press play on potential? 

Let's start with the role

At Kaizen, we aim to leverage the capabilities offered by modern AI algorithms to continuously improve our product and business. Kaizen’s AI department consists of product, platform and research teams and lies at the core of that mission. We develop AI products for various applications, ranging from detection of fraudulent activity to personalized gaming recommendations, and from AI-powered co-pilots to CRM optimizations. 

We build our teams to be self-driven, autonomous and cross-functional. Within these teams, you will have the opportunity to work with Data Scientists, Machine Learning Engineers, Data Engineers, Data Analysts, Product Owners and Agile Delivery Leads.

As a Machine Learning Software Engineer, you will focus on delivering robust and scalable Machine Learning pipelines and you will support the operationalization of Machine Learning models. 

• Design, develop and maintain scalable model training and inference pipelines in a distributed production environment;
• Design, develop and maintain microservices in Python that host and serve ML models on production;
• Design, develop and maintain internal tools that enable CI/CD/CT, performant feature engineering, experiment tracking, data and model versioning.

What you’ll bring

• Ηands-on experience in software development using Python on a production environment;
• Good understanding of:
• Software system design; 
• The ML project lifecycle;
• MLOps concepts and tools such as MLflow or Kubeflow;
• ML algorithms and models.

• Experience with cloud providers like Microsoft Azure or AWS;
• Experience with PySpark and Databricks.

#Machine Learning (ML) #MLOps #AIOps #Software Engineering

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