[Data Engineer] Senior / Staff MLOps Engineer (Bangkok Based, relocation provided)

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 experi...

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.

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, and to develop MLOps solutions that meet those needs
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
Mentor junior members of the team and help them grow their skills and expertise in MLOps
Troubleshooting and debugging user issues

 

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 (jobs, services, or ML/training pipelines), debugging pod failures, and working with core concepts (Deployments, ConfigMaps/Secrets, Services, resource limits)
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 etc.
Experience with Big Data tools – Spark, S3, Hadoop
Strong knowledge of containerization and container orchestration technologies, such as Docker and Kubernetes
Experience with DevOps and CI/CD practices
Has experience building and scaling model serving tools, i.e building APIs with tight SLAs
Passion for the engineering behind machine learning, and scaling it
Experience designing and building MLOps infrastructure, including data pipelines, model training and deployment systems, and monitoring and management tools.

 
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