MLOpsMachine learningKubernetesData engineeringPythonJavaKotlinScalaDockerCI/CDPyTorchTensorFlow
About Agoda
At Agoda, we bridge the world through travel. Our story began in 2005, when two lifelong friends and entrepreneurs, driven by their passion for travel, launched Agoda to make it easier for everyone to explore the world.
Today, we are part of Booking Holdings [NASDAQ: BKNG], with a diverse team of over 7,000 people from 90 countries, working together in offices around the globe. Every day, we connect people to destinations and experiences, with our great deals across our millions of hotels and holiday properties, flights, and experiences worldwide.
No two days are the same at Agoda. Data and technology are at the heart of our culture, fueling our curiosity and innovation. If you’re ready to begin your best journey and help build travel for the world, join us.
ML Platform in Agoda
Agoda ML and AI Platform is built on self-managed Kubernetes and powered by Kubeflow, an open-source solution that enables efficient machine learning workflows.
Our AI Ops 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:
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Lead the design, development, and implementation of on-premises MLOps solutions that support the delivery of machine learning models
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Collaborate with data scientists and software engineers to build scalable and efficient data pipelines and model training and deployment systems
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Develop and maintain monitoring and management tools to ensure the reliability and performance of our on-premises MLOps infrastructure
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Work with stakeholders across the organization to understand their machine learning needs and requirements, and to develop MLOps solutions that meet those needs
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Stay up-to-date with the latest trends and technologies in MLOps, machine learning, and artificial intelligence, and share your knowledge with the team to help us stay at the forefront of the field
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Mentor junior members of the team and help them grow their skills and expertise in MLOps
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Troubleshooting and debugging user issues
What You’ll Need to Succeed
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5+ years of experience in MLOps, Software engineering, Data engineering, or a related field
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Strong programming skills in a modern programming language (Java, Scala, Python, Kotlin)
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Hands-on experience with Kubernetes—deploying and operating workloads (jobs, services, or ML/training pipelines), debugging pod failures, and working with core concepts (Deployments, ConfigMaps/Secrets, Services, resource limits)
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Excellent communication and collaboration skills, and the ability to work effectively in a team environment
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Value code simplicity and performance
It’s Great if you have:
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Understanding of ML Projects lifecycle
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Experience with MLOps platforms, such as Kubeflow or MLFlow
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Experience with ML frameworks – like scikit-learn, LGBM, Tensorflow, PyTorch etc.
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Experience with Big Data tools – Spark, S3, Hadoop
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Strong knowledge of containerization and container orchestration technologies, such as Docker and Kubernetes
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Experience with DevOps and CI/CD practices
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Has experience building and scaling model serving tools, i.e building APIs with tight SLAs
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Passion for the engineering behind machine learning, and scaling it
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Experience designing and building MLOps infrastructure, including data pipelines, model training and deployment systems, and monitoring and management tools.
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Please review our Hiring Process Guidelines before your interview — click here to learn how interviewing at Agoda works.
Discover more about working at Agoda
• Agoda Careers https://careersatagoda.com
• Facebook https://www.facebook.com/agodacareers/
• LinkedIn https://www.linkedin.com/company/agoda
• YouTube https://www.youtube.com/agodalife
Equal Opportunity Employer
At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting div