Senior / Staff MLOps Engineer
Agoda →Bangkok, Thailand
Estimated range. Confirm compensation with the employer.
Bangkok, Thailand
Estimated range. Confirm compensation with the employer.
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Listing record updated: October 17, 2026. We archive records after 60 days without an update. This date does not confirm that the employer is still hiring.
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Agoda is hiring a Senior/Staff MLOps Engineer to join its ML and AI Platform team in Bangkok, with relocation provided. The role focuses on designing, developing, and operating on-premises MLOps infrastructure built on self-managed Kubernetes and Kubeflow, supporting machine learning model delivery across the organization.
Responsibilities include building scalable data pipelines, model training and deployment systems, monitoring tools, and collaborating with data scientists and software engineers. The role also involves mentoring junior team members, troubleshooting user issues, and staying current with MLOps trends. Candidates need 5+ years of relevant experience, strong programming skills, and hands-on Kubernetes expertise.
Lead the design, development, and implementation of on-premises MLOps solutions that support the delivery of machine learning models
Collaborate with data scientists and software engineers to build scalable and efficient data pipelines and model training and deployment systems
Develop and maintain monitoring and management tools to ensure the reliability and performance of our on-premises MLOps infrastructure
Work with stakeholders across the organization to understand their machine learning needs and requirements
Mentor junior members of the team and help them grow their skills and expertise in MLOps
5+ years of experience in MLOps, Software engineering, Data engineering, or a related field
Strong programming skills in a modern programming language (Java, Scala, Python, Kotlin)
Hands-on experience with Kubernetes—deploying and operating workloads, debugging pod failures, and working with core concepts
Excellent communication and collaboration skills, and the ability to work effectively in a team environment
Value code simplicity and performance
Understanding of ML Projects lifecycle
Experience with MLOps platforms such as Kubeflow or MLFlow
Experience with ML frameworks like scikit-learn, LGBM, Tensorflow, PyTorch
Experience with Big Data tools – Spark, S3, Hadoop
Experience designing and building MLOps infrastructure, including data pipelines, model training and deployment systems
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