[Cooperative Internship 2025] MLOps Engineer Intern

Cooperative Internship Period: January - May 2025

The Opportunity 

Agoda ML Platform is built on self-managed Kubernetes and powered by Kubeflow, an open-source solution that enables efficient machine learning workflows.  

Our MLOps team combines the best practices of software engineering with data science to help Machine Learning Engineers and Data Scientists work more effectively, meaning we aim to incorporate data/model versioning, collaboration, monitoring etc. in an intuitive way that allows our users to prosper within the field. 

In this Role, you’ll get to: 

• Assist in the design and development of on-premises MLOps solutions under the guidance of senior team members to support the delivery of machine learning models. 
• Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training and deployment systems. 
• Contribute to the development and maintenance of monitoring and management tools for the on-premises MLOps infrastructure. 
• Engage with various stakeholders within the organization to gather insights into their machine learning needs and requirements and observe how MLOps solutions are developed to meet those needs. 
• Stay informed about the latest trends and technologies in MLOps, LLMOps, machine learning, and artificial intelligence, with opportunities to learn from experts within the field. 
• Receive mentoring from senior members of the team to grow your skills and expertise in MLOps and related areas. 

What you’ll Need to Succeed: 

• Currently enrolled in or a recent graduate of a degree program in Computer Science, Software Engineering, Data Science, or a related field. 
• Strong desire to learn and good communication skills, with an enthusiasm for collaborative problem-solving. 
• Basic programming skills in a modern programming language (Java, Scala, Python, Kotlin). 

It’s Great if you have: 

• Exposure to any MLOps platforms, such as Kubeflow or MLFlow, either through coursework, projects, or internships. 
• Familiarity with any Data Analytics or ML frameworks – like numpy, scipy, pandas, scikit-learn, Tensorflow, PyTorch – gained through academic projects or self-learning. 
• Some knowledge of Big Data tools – Spark, S3, Hadoop – from classes, projects, or internships. 
• Awareness of containerization and container orchestration technologies, such as Docker and Kubernetes, from coursework or hobby projects. 
• An understanding of DevOps and CI/CD practices through academic exposure or personal projects. 
• Any experience in creating APIs or working with web services is a plus. 
• A keen interest in machine learning engineering and a willingness to explore how it can be scaled effectively. 
• Curiosity about designing and building MLOps infrastructure components, such as data pipelines, model training systems, and monitoring tools. 

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